Human trajectory prediction Considering the indeterminate nature of human motion, this task is particularly challenging due to the necessity of predicting the precise coordinates of the positions over all timesteps, which requires addressing both the short-term dynamics and long Oct 1, 2022 · Motivated by the advantages mentioned above, we provide a flow-based architecture in this work for human trajectory prediction, where an invertible network serves as the core part, accounting for the multi-modal characteristics of trajectory data. 34, 30380 Mar 24, 2024 · The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. Dec 4, 2024 · This paper proposes SITUATE, a novel approach to cope with indoor human trajectory prediction by leveraging equivariant and invariant geometric features and a self-supervised vision representation. The Mar 6, 2025 · Conventional human trajectory prediction models rely on clean curated data, requiring specialized equipment or manual labeling, which is often impractical for robotic applications. 7k次，点赞6次，收藏25次。Social LSTM:Human Trajectory Prediction in Crowded Spaces 翻译近期学习研究相关方向论文，Social LSTM算是比较经典的一篇，阅读过程中简要翻译，分享给有同样阅读需要的人，翻译比较简单，仅供 Mar 20, 2024 · The task is known as human trajectory prediction and aims to predict the future positions of humans using their past positions as input data. However, most of the existing methods ignore the Mar 21, 2024 · Trajectory prediction plays an essential role in autonomous vehicles. Learning Pedestrian Group Representations for Multi-modal Trajectory Prediction. Moreover, human joints’ movements are tightly cou-pled in the skeleton level and between interacting individu-als. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for prediction, considering inevitable factors such as tracking losses and sudden events. , Claudel, C. , t−7,t−6,,t, in world plane coordinates (the so-called world plane Human-Human protocol) and forecasting the following 12 (4. Though significant progress has been made over the past years, human trajectory prediction remains a very challenging problem and continues to Jul 30, 2021 · We propose a counterfactual analysis method to alleviate the overdependence of environment bias and highlight the trajectory clues itself. Overlooked Poses Actually Make Sense: Distilling Privileged Knowledge for Mar 15, 2022 · Predicting pedestrian movement is critical for human behavior analysis and also for safe and efficient human-agent interactions. For example, Alahi et al. Updated Apr 4, 2024; Python; Sep 4, 2023 · LSTM Networks for 3D Human Trajectory Prediction [16]: this method uses an ensemble of LSTM networks with an attention mechanism to predict 3D human trajectories by selec-tively focusing on relevant parts of the input data. 2022. To solve the completion or recovery problem, it is usually necessary and helpful to learn the missing locations Action prediction is a pre-fact video understanding task, which focuses on future states, in other words, it needs to reason about future states or infer action labels before the end of action execution. Specifically, we focus on ground-level 2D trajectory prediction for pedestrians and also Feb 26, 2021 · Human Trajectory Prediction (HTP) has gained much momentum in the last years and many solutions have been proposed to solve it. Our model can learn general human mobility patterns and predict individual’ s trajectories based on their past positions, in particular, with the influence of their neighbors in the Social Affinity Map (SAM). The backbone of Social LODE consists of a conditional Variational Autoencoder (VAE) architecture based on Jun 29, 2020 · diction method for human trajectory prediction. However, the literature is still sparse in providing practical frameworks that enable mobile manipulators to perform whole-body movements and account for the predicted motion of moving obstacles. Because of the continuity and foresight of the pedestrian movements, the moving pedestrians in crowded spaces will consider both spatial and temporal interactions to avoid future collisions. This concept becomes particularly Jan 2, 2021 · Social LSTM:Human Trajectory Prediction in Crowded Spaces 翻译 近期学习研究相关方向论文，Social LSTM算是比较经典的一篇，阅读过程中简要翻译，分享给有同样阅读需要的人，翻译比较简单，仅供参考。 Social LSTM:Human Trajectory Prediction in Jul 4, 2022 · Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. 759–776. However, human behaviour is naturally multimodal and uncertain: given the past trajectory and surrounding environment information, May 8, 2023 · requires human trajectory forecasting systems to formulate humans’ multimodality nature and infer not a single future state but the full range of plausible ones [16,32]. One of the most challenging tasks is to model the interaction between pedestrians. Hu- Jul 9, 2024 · Human trajectory prediction is a practical task of predicting the future positions of pedestrians on the road, which typically covers all temporal ranges from short-term to long-term within a trajectory. Unlike GAN-based models, the proposed framework can learn the multi-modal distribution explicitly. Apr 8, 2023 · Human Trajectory Prediction via Neural Social Physics 3 2 Related Work 2. arXiv Code. In the developed multimodal data-based human motion intention prediction model, EEG/EMG signals and sensor measures are used for the validation, and their details Feb 11, 2025 · Trajectory forecasting for human mobility plays a critical role in the effective management and sustainable development of urban transportation, which aligns with the advocacy of Sustainable Development Goals (SDGs). Therefore, many recent works devote to multi-modal trajectory predictions by employing generative models or probabilistic models, such as Generative Adversarial Network (GAN) [7] and Conditional Variational To achieve safe and efficient human-robot interaction(HRI), it is essential to predict how operators will move during the execution of tasks by both humans and robots. This problem of trajectory prediction can be viewed as a sequence generation task, where we are interested in predicting Apr 1, 2024 · Therefore, this research proposes a novel activity-aware prediction model for 3D human motion trajectory. Jan 18, 2023 · It is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction Published at ECCV 2020 (Oral). The goal of human trajectory prediction is to predict a set of 2D coordinates for each human characterizing its global motion. We addressed the problem of generalizability, proposing new datasets characterized by higher diversity and lower linearity than the literature dataset and proposing new metrics to Jul 1, 2020 · This article provides a survey of human motion trajectory prediction. In this work, we propose MonoTransmotion (MT), a Transformer Aug 18, 2020 · Human trajectory prediction is challenging and critical in various applications (e. Jun 7, 2020 · This article provides a survey of human motion trajectory prediction. One approach to tackle this challenge is using knowledge-based approaches (Korbmacher & Tordeux, 2022) that model pedestrian dynamics for trajectory prediction using specific methods involving physical or psychological Apr 3, 2023 · Human Trajectory Prediction via Neural Social Physics 3 2 Related Work 2. They aim to learn individual motion dynamics [76], structured latent patterns in data [65,64], anomalies [12,11], etc. [7] point out that there are many socially plausible ways that people could move in the future. Jun 6, 2022 · Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. Here we summarize the most relevant methods for human trajectory forecasting and multi-target tracking. 1st row shows an example where the pedestrian exhibits 2 modes of motion (walking and running) within the same trajectory. 1 Trajectory Analysis and Prediction Statistical machine learning has been used for trajectory analysis in computer vision [42,15,67,26,65,11]. While these efforts have provided the community with multi-dataset benchmarks, they are primarily focused on pedestrian data. Traditional works predict pedestrian trajectories using deterministic models [19, 18, 34, 21, 7]. Prediction methods started from simple models such as social forces [5, 15], and later added multimodality through Gaussian processes []. The former focuses on predicting scene-specific human movement patterns [4, 10, 24, 29, 39] and takes advantage of the scene environment information, typically through the use of semantic maps. While this paradigm has yielded tremendous progress, it fundamentally assumes that trends in human behavior within the deployment scene are constant Nov 2, 2018 · Trajectory prediction is a tough work because of the complexity of the situation. 2021. However, despite significant advancements, it is still challenging for existing approaches to capture the uncertainty and multimodality of human navigation decision making. The geometric learning modules model the intrinsic symmetries and human movements inherent in indoor spaces. To name a few, in the red box, some pedestrians are staying there without moving, and in the yellow box and green box, there are lots of pedestrians exhibiting cooperative behavior. Interpretable Social Anchors for Human Trajectory Forecasting in Crowds4. Trajectory Prediction via Conditional 3D Attention In this section, we develop a sequence to sequence framework for human trajectory prediction that leverages video data directly to infer human-dependent interactions using a conditional 3D attention mechanism. Deep learning approaches have become key in this area, utilizing large-scale trajectory datasets to model movement patterns, but face challenges in managing Jun 24, 2021 · Despite recent surge of studies on human path prediction, most works focus on static scene information, therefore, cannot leverage the rich dynamic visual information of the scene. 961-971 Abstract. While this paradigm has yielded tremendous progress, it fundamentally assumes that trends in human behavior within the deployment scene are constant over time. We Jan 14, 2025 · Their fundamental structure has made them unreliable for long-term prediction. D espite the recentprogress,trajectory prediction is stilla challenging problem due to com plex socialor physicalenvi-ronm ent interactions. Some studies formulate trajectory prediction as a sequence prediction problem and use recurrent neural networks (RNNs) to model human Jul 1, 2021 · Finally, some works from the domain of human trajectory prediction propose interesting methods that could be extended to the domain of vehicle trajectory prediction. Shafiee_2021_CVPR, author = {Shafiee, Nasim and Padir, Taskin and Elhamifar, Ehsan}, title = {Introvert: Human Trajectory Prediction via Conditional 3D Attention Figure 1: Adaptive trajectory prediction. Jul 30, 2021 · Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots. An exhaustive study of crowd analysis is introduced by Treuille et al. Sep 26, 2021 · predictions to alleviate the negative effects of environm ent bias. Trajectory Prediction of Many Agents with Multimodal Attention and Graph Embedding [17]: This method uses a graph embedding Aug 26, 2024 · Human trajectory prediction is a crucial yet challenging problem, which is of fundamental importance to robotics and autonomous driving vehicles. However, existing works attempt to address the entire trajectory prediction with a singular, uniform training paradigm, neglecting the distinction between short-term and long Aug 1, 2023 · Over the years, the separate fields of motion planning, mapping, and human trajectory prediction have advanced considerably. Unlike other object movements with rule Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction Abstract: Better machine understanding of pedestrian behaviors enables faster progress in modeling interactions between agents such as autonomous vehicles and humans. However, the moving patterns of human in a constrained scenario typically conform to a limited number of regularities to a certain extent, because of the scenario restrictions and person-person or person-object interactivity. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Springer (2020) [39] Mohamed, A. The rest of the paper is organized as follows. We provide an overview of the existing datasets and Mar 16, 2021 · Social LSTM:Human Trajectory Prediction in Crowded Spaces 翻译 近期学习研究相关方向论文，Social LSTM算是比较经典的一篇，阅读过程中简要翻译，分享给有同样阅读需要的人，翻译比较简单，仅供参考。 Social LSTM:Human Trajectory Prediction Nov 11, 2021 · Abstract: Human trajectory prediction is an essential and challenging task in robot navigation and autonomous driving applications. MobTCast: Leveraging auxiliary trajectory forecasting for human mobility prediction. B. We provide an overview of the existing datasets and Nov 6, 2023 · We introduce existing datasets for Human Trajectory Prediction (HTP) task, and also provide tools to load, visualize and analyze datasets. Many studies adopt spatial-temporal graph neural networks (STGNNs) for the sequence prediction task, such as action recognition [48, 35], taxi demand prediction [50], and traf-fic prediction [49]. 122423 239:C Online publication date: 1-Apr-2024. The study by Fujii et al. Feb 21, 2023 · Trajectory prediction is an important task to support safe and intelligent behaviours in autonomous systems. 56%-70%. Thus, an Dec 28, 2024 · Human trajectory forecasting is a crucial challenge in the social security and autonomous driving due to the inherent uncertainty in human actions and intentions. Google Scholar [2] Inhwan Bae, Jin-Hwi Park, and Hae-Gon Jeon. Recent works based on long-short term memory (LSTM) models have brought tremendous improvements on the task of trajectory Oct 29, 2019 · • Social LSTM: Human Trajectory Prediction in Crowded Spaces 摘要：比较早的斯坦福大学2016年工作。行人遵循不同的轨迹避开障碍物并接纳其他行人。在这样的场景中任何自动驾驶汽车都应该能够预见行人的未来位置，并相应地调整行进路线避免碰撞。 Dec 2, 2019 · 文章浏览阅读3. While numerous strategies have been developed to enhance the robustness of trajectory prediction models, these methods are predominantly heuristic and do not offer guaranteed robustness against adversarial attacks and noisy observations. Any autonomous vehicle navigating such a scene should be able to foresee the future positions of pedestrians and accordingly adjust its path to avoid collisions. However, existing works attempt to address the entire trajectory prediction with a singular, uniform training paradigm, neglecting the It is not the journey but the destination: Endpoint conditioned trajectory prediction. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Human trajectory predictions. In contrast, trajdata tackles the standardization of both pedestrian and autonomous vehicle datasets, including additional data modalities such as maps. Pedestrian trajectories are not only influenced by the pedestrian itself Jun 7, 2022 · 3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Existing research in human-robot interaction often focuses solely on human hand predictions, frequently overlooking the significance of robotic arm movements in maintaining safety. Specifically, the sequence can be for-mulated as a sequence of graphs of nodes and edges, where The TrajNet Challenge represents a large multi-scenario forecasting benchmark. Oct 24, 2021 · Hence, we propose a counterfactual analysis method for human trajectory prediction to investigate the causality between the predicted trajectories and input clues and alleviate the negative effects brought by environment bias. The HLTP model incorporates a sophisticated teacher-student knowledge distillation framework. , for self-driving cars and social robots. Furthermore, they need to know the uncertainty of the predictions for risk assessment to Predicting human motion is an extremely challenging problem due to complex and subtle interactions between pedestrians in crowded places. Jun 1, 2021 · Introvert is proposed, a model which predicts human path based on his/her observed trajectory and the dynamic scene context, captured via a conditional 3D visual attention mechanism working on the input video. Jun 11, 2021 · In this work, we pro-pose Introvert, a model which predicts human path based on his/her observed trajectory and the dynamic scene context, captured via a conditional 3D Jun 1, 2022 · Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for predic- Jul 16, 2024 · Human trajectory prediction is a practical task of predicting the future positions of pedestrians on the road, which typically covers all temporal ranges from short-term to long-term within a trajectory. Framework for Long-Term Cross-City Mobility Prediction Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Human Mobility Prediction Challenge 10. These road-agents have Oct 1, 2024 · Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of human motion in training scenes, and then deployed on unseen test scenes. Section 2 reviews related work on human trajectory prediction. To address this, we introduce Social-Transmotion, a generic Transformer-based Dec 18, 2022 · 整理基于深度学习的轨迹预测论文，其中包括行人和车辆的轨迹预测方法，另外包括代码实现，后续会加上论文解读，会持续进行更新。 1. Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of hu- Therefore, we present a Human-Like Trajectory Prediction (HLTP) model that emulates human cognitive processes for improved trajectory prediction in AVs. Introvert: Human Trajectory Prediction via Conditional 3D Attention2. It can effectively mitigate potential collision risks and has a broad application in intelligent autonomous vehicles [] and mobile robots [], etc. Statistical machine learning has been used for trajectory analysis in computer vision [11, 15, 26, 42, 64, 66]. 3699910 (10 Oct 28, 2024 · Social lstm: Human trajectory prediction in crowded spaces. This counterfactual analysis method is a plug-and-play module which can be easily applied to any baseline predictor, and consistently improves the performance on many human trajectory prediction benchmarks. Thanks to a large number of applications like predicting abnormal events, navigation system for the blind, etc. (b) Image Segmentation Output. proposed a two-block RNN for trajectory prediction from a partial trajectory due to misdetection, which learned the inference step of Bayesian filters. 961-971. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for prediction, considering inevitable factors such as We present a novel approach for long-term human trajectory prediction in indoor human-centric environments, which is essential for long-horizon robot planning in these environments. Jun 29, 2020 · Human trajectory prediction is essential and promising in many related applications. Proper benchmarking being a key issue for comparing methods, this paper addresses the question of evaluating how complex is a given dataset with respect to the prediction problem. These methods provide a certain level of explainability, but are limited in model capacity for learning Oct 27, 2019 · Human trajectory prediction is challenging and critical in various applications (e. However, many trajectory prediction models produce unreasonable trajectory samples that focus on improving diversity or accuracy while neglecting other key requirements, such as collision avoidance with Dec 1, 2021 · Younesi Heravi M Jang Y Jeong I Sarkar S (2024) Deep learning-based activity-aware 3D human motion trajectory prediction in construction Expert Systems with Applications: An International Journal 10. We first build a causal graph for trajectory forecasting with history trajectory, future trajectory, and the Oct 30, 2024 · Human trajectory prediction aims to forecast the reasonable future path given an observed sequence of movements. Human trajectory forecasting algorithms try to estimate or predict this path. The challenge consists on predicting 3161 human trajectories, observing for each trajectory 8 consecutive ground-truth values (3. Trajectory prediction has received significant attention, and the recent data-driven methodologies have exhibited remarkable performance [20, 45]. 34, 30380 Feb 17, 2025 · prediction dataset self-driving-car trajectory-prediction crowd-analysis person-tracking human-trajectory-prediction motion-prediction trajectory-prediction-benchmark Updated Apr 4, 2024 Python In this work, we focus on the task of human trajectory prediction from short-term historical observations. Publicly Available Datasets Nov 4, 2024 · Keywords: Human motion prediction, Trajectory prediction, Pose prediction, Multimodal pre-trained model, Multitask pre-trained model 1 Introduction The research community has witnessed substantial advancements through the adoption of pre-trained models. Sep 14, 2022 · Given a history motion path of human, Gupta et al. stanford. In crow ded environm ent, the Aug 11, 2021 · the human trajectory prediction method introduced in [7]. , autonomous vehicles and social robots). The first module identifies the coordinates of the joints and employs a data fusion technique to derive the three-dimensional locations of an object’s body joints. Dec 11, 2023 · The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. pp. Aug 13, 2018 · We develop a human movement trajectory prediction system that incorporates the scene information (Scene-LSTM) as well as human movement trajectories (Pedestrian movement LSTM) in the prediction process within static crowded scenes. Our model consists of Jul 29, 2024 · Evaluating Human Trajectory Prediction with Metamorphic Testing MET ’24, September 17, 2024, Vienna, Austria (a) Inputs and Outputs. hum an trajectory prediction task has attracted m uch atten-tion over the past few years [1,53,56,15,25]. We host the Trajnet++ Challenge on AICrowd allowing researchers to objectively evaluate and benchmark trajectory forecasting models on interaction-centric data. Authors: Yiwen Song, Jingtao Ding Flora Salim, Yongli Ren, and Nuria Oliver. Many advanced approaches have been proposed over the years with improved spatial and temporal feature extraction. As is shown in Fig. May 15, 2019 · Social LSTM:Human Trajectory Prediction in Crowded Spaces 翻译 近期学习研究相关方向论文，Social LSTM算是比较经典的一篇，阅读过程中简要翻译，分享给有同样阅读需要的人，翻译比较简单，仅供参考。Social Dec 1, 2018 · Example scenarios from the GC dataset (Yi et al. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when navigating the space. The proposed prediction model is composed of three modules. [2] Alahi, Alexandre, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, and Dec 28, 2024 · Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion. In real-world scenarios, it is unlikely to obtain sufficiently long observations at all times for prediction, considering inevitable factors such as Aug 12, 2024 · BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving——AAAI 2024 Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism——IEEE TIM 2022 Jan 23, 2023 · Human motion comes in many forms: articulated full body motion, gestures and facial expressions, or movement through space by walking, using a mobility device or driving a vehicle. (left) Given a history of human behavior (shown in black), the pre-trained predictor 𝒫 𝒫 \mathcal{P} is unable to understand scene-specific behavior trends, like people entering a subterranean subway entrance (bottom row). · Code for "Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction" CVPR 2020. in propose the Social LSTM, a Human trajectory prediction is essential and promising in many related applications. Jul 22, 2022 · We compare NSP with 15 recent deep learning methods on 6 datasets and improve the state-of-the-art performance by 5. The scope of this survey is human motion trajectory prediction. Our ex-perimental results on benchmark datasets demonstrate that our new method outperforms the state-of-the-art methods for human trajectory prediction. The core challenge lies in effectively modeling the socially aware spatial interaction and complex temporal dependencies among crowds. In European Conference on Feb 5, 2025 · requires human trajectory forecasting systems to formulate humans’ multimodality nature and infer not a single future state but the full range of plausible ones [16,32]. Human trajectory forecasting. Although several approaches have been developed in other trajectory forecasting applications, such as autonomous driving and Jun 1, 2022 · Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. This predictive process finds its applicability and utility in a multitude of domains []. It is a critical and fundamental task for many applications, including the planning and controlling of the autonomous vehicles, the robot navigation, and the tracking and re-identification in the Dec 1, 2021 · In this work, we addressed different challenges of the human trajectory prediction problem, including some aspects that have been neglected in state-of-the-art works. In this paper, we propose a new method Sep 17, 2016 · A large variety of methods has been proposed in the literature to describe, model and predict human behaviors in a crowded space. An LSTM consists of a memory cell 𝑐, an input gate 𝒾, an output gate ℴ, and a forget gate 𝒻. Oct 9, 2024 · Autonomous systems, like vehicles or robots, require reliable, accurate, fast, resource-efficient, scalable, and low-latency trajectory predictions to get initial knowledge about future locations and movements of surrounding objects for safe human-machine interaction. Most existing methods learn to predict future trajectories by behavior clues from history trajectories and interaction clues from environments. The former include rule-based, geometric or optimization-based models, and the latter are mainly comprised of deep learning approaches. Our method incorporates Time2Vec to capture both periodic and trend-based temporal features and utilizes CatBoost to handle structured, non-sequential trajectory data efficiently. Oct 28, 2024 · 4篇cvpr2021 轨迹预测论文1. Recently, modern learning-based predictors have made significant progress in incorporating social interactions between humans modelled via RNNs [17, 39, 44], May 20, 2022 · Human trajectory prediction is a crucial yet challenging problem, which is of fundamental importance to robotics and autonomous driving vehicles. In: European Conference on Computer Vision. Addressing safety concerns in Dec 31, 2024 · Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data, benefiting fields like autonomous navigation, robotics, and human movement analytics. . RNNs, most notably, LSTM, are now widely used in modeling to anticipate human motions. We superimpose a two-level grid structure (scene is divided into grid cells each modeled by a scene-LSTM, which are further Nov 27, 2024 · 【ECCV 2024】Progressive Pretext Task Learning for Human Trajectory Prediction_progressive pretext task learning for human trajectory prediction 【论文阅读笔记】PPT ：基于渐进式代理任务学习的行人轨迹预测 xyy_wow 已于 2024-11-27 11:52:05 修改 收藏 Trajectory Prediction is the problem of predicting the short-term (1-3 seconds) and long-term (3-5 seconds) spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and animals, etc. 8 seconds), i. We review, analyze, and structure a large selection of work from different communities and propose a May 20, 2022 · We propose a novel trajectory prediction framework termed GA-STT, a group aware spatial-temporal transformer network to address these issues. (right) When adapting, the number of people and amount of time determine the total number of trajectories observed, and we Dec 1, 2017 · Human Trajectory Prediction Dataset Benchmark (ACCV 2020) prediction dataset self-driving-car trajectory-prediction crowd-analysis person-tracking human-trajectory-prediction motion-prediction trajectory-prediction-benchmark. Hence, Sep 27, 2022 · Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars and social robots. 3. In this work, we are interested in the latter, which Mar 19, 2024 · Human trajectory prediction aims at forecasting the future trajectory of pedestrians based on their past positions in complex and crowd environments. 1. The core challenge lies in effectively modeling the socially aware spatial interaction and May 13, 2024 · Human trajectory data produced by daily mobile devices has proven its usefulness in various substantial fields such as urban planning and epidemic prevention. The teacher model, equipped with an adaptive visual sector, mimics the visual processing of the Apr 4, 2017 · Human Trajectory Prediction in Crowded Spaces Alexandre Alahi∗, Kratarth Goel∗, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese Stanford University {alahi,kratarth,vigneshr,arobicqu,feifeili,ssilvio}@cs. Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. They aim to learn individual motion dynamics [], structured latent patterns in data [63, 64], anomalies [11, 12], etc. g. In Proceedings of the Advances in Neural Information Processing Systems, Vol. Sep 26, 2021 · ing. Pairwise attention is used by most of the Dec 24, 2024 · Human motion intention dataset. The memory cell stores and Jun 11, 2021 · 3. Pedestrians follow different trajectories to avoid obstacles and accommodate fellow pedestrians. ecasting in Sep 23, 2022 · The human mobility trajectory completion problem bares certain resemblance to the trajectory prediction problem in practice. It is a critical and fun-damental task for many applications, including the planning and Nov 28, 2022 · A human trajectory is the likely path a human subject would take to get to a destination. Abstract: Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e. State-of-the-art human trajectory prediction methods are limited by their focus on collision avoidance and short-term planning, and their inability to model complex interactions of humans with the Oct 18, 2024 · In summary, we make the following contributions: For the first time, we study long-term human trajectory prediction in complex, human-centric indoor environments with a prediction horizon of up to 60 s times 60 s 60\text{\,}\mathrm{s} start_ARG 60 end_ARG start_ARG times end_ARG start_ARG roman_s end_ARG, including complex human-object interactions. In this work, we propose a certification Jan 11, 2024 · 原文链接：Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction 代码链接：代码有一段时间没有更新博客了，这一段时间我在干嘛呢？ 回想起来除了每周定时的007外，只是单纯的懒。在写作风格 Dec 9, 2020 · A Spatial-Temporal Attention Model for Human Trajectory Prediction摘要1 引言2 相关工作3 Method4 Experiments5 Discussion6 Conclusions Human Trajectory Prediction) 人体轨迹预测的时空注意模型 作者：Xiaodong Zhao, Yaran Chen, 论文地址：IEEE/CAA Oct 28, 2022 · Research in pedestrian trajectory prediction can be broadly classified into human-space and human-human models. However, human navigation behaviors have an Jul 29, 2023 · human motion trajectory prediction algorithms in a unified framework. eswa. We review, analyze and structure a large selection of work from different communities and propose a Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendr Accepted at ECCV 2020 (Oral) · Nov 6, 2023 · We introduce existing datasets for Human Trajectory Prediction (HTP) Jun 1, 2022 · Human trajectory prediction task aims to analyze human future movements given their past status, which is a crucial step for many autonomous systems such as self-driving cars Jul 1, 2020 · This article provides a survey of human motion trajectory prediction. arXiv Code Website. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and . , 2015): Columns (left to right): first observation of the trajectory; half way through; last observation prior to prediction; prediction from the respective models. Facing this challenge, many prior methods formulate stochastic human trajectory prediction as a generative prob-lem, in which a latent random variable is used to represent Aug 18, 2020 · Social LSTM: Human Trajectory Prediction in Crowded Spaces. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. , t+1,,t+12. In this paper, we propose SocialVAE, a novel approach for human Apr 16, 2024 · Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. These meth- Dec 4, 2024 · Human trajectory prediction is the task of predicting the likely path that a subject will take to reach its designated endpoint []. So far multiple datasets are supported. The existing predictors tend to overfit to clean observation affecting their robustness when used with noisy inputs. However, previous methods usually model spatial and temporal information Dec 28, 2023 · Human trajectory prediction is an important topic in several application do- mains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent Human trajectory prediction is crucial in human-computer interaction and even in the safety of autonomous driving. Social LSTM（2016） paper： Social LSTM:Human Trajectory Prediction in Cro Forecasting human trajectories in complex dynamic environments plays a critical role in autonomous vehicles and intelligent robots. SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction3. However, most of the Apr 12, 2016 · Human Trajectory Prediction in Crowded Spaces Alexandre Alahi , Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese to understand and predict human motion in complex real world environments is extremely valuable for a wide range of applications - from the deployment of socially-aware robots [41] to the design Dec 11, 2023 · Human trajectory prediction is typically posed as a zero-shot generalization problem: a predictor is learnt on a dataset of human motion in training scenes, and then deployed on unseen test scenes. In the past few years, human trajectory prediction based on deep learning methods has been extensively studied and significant progress has been made. Human Mar 21, 2022 · Human Trajectory Prediction via Neural Social Physics. realtime graph-convolutional-networks gcnn pedestrians graph-neural-networks human-trajectory-prediction pedestrian-trajectories spatio-temporal-graphs social-stgcnn. 2nd row Jan 29, 2021 · Social LSTM: Human Trajectory Prediction in Crowded Spaces论文解读 论文地址： code(pytorch): 写在前面 刚接触这一方向，该论文属于行人轨迹 Mar 24, 2020 · 1、Human trajectory prediction using deep models Social-LSTM使用一个递归网络对每个行人的运动进行建模，然后使用一个聚合机制对递归输出进行聚合，然后对轨迹进行预测。Social-LSTM假设行人轨迹服从双变量高斯分布，在我们的模型中遵循这一假设。 Dec 16, 2024 · To address these challenges, we propose a novel feature-engineering and machine learning-based framework for trajectory prediction. 1 Trajectory Analysis and Prediction. Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars. The problem is best formulated in a social manner. 1145/3681771. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Facing this challenge, many prior methods formulate stochastic human trajectory prediction as a generative prob-lem, in which a latent random variable is used to represent Jun 7, 2020 · This article provides a survey of human motion trajectory prediction. We rely on the spirit of crowdsourcing and the challenge has > 1800 submissions. However, the inherent bias between training and deployment environments is ignored. " In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. Dec 28, 2024 · Controllable Human Trajectory Generation Using Profile-Guided Latent Diffusion. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods Introvert: Human Trajectory Prediction via Conditional 3D Attention Abstract: Predicting human trajectories is an important component of autonomous moving platforms, such as social robots and self-driving cars. 961--971. In: Proceedings of Sep 1, 2019 · Human trajectory prediction remains a core problem in computer vision with numerous applications, such as social-aware robots [1], intelligent tracking system [2], [3], object detection and tracking [4], [5], [6], and so on. Blue is the past history, red is the pre-dicted trajectories, yellow is the ground-truth trajectory. Traditional approaches often rely on computational methods such as time-series analysis. These meth- Dec 27, 2022 · Human trajectory prediction is a key module for any autonomously navigating system and its applications cover a wide range from mobile robot navigation, including autonomous driving, smart video surveillance to object tracking. , Qian, K. 1016/j. In this work, A new method, called Social Latent Ordinary Differential Equation (Social LODE), is introduced for predicting human trajectories. 2016. 1, they are the \(7340^{th}\) and the \(7380^{th}\) frame in dataset []. Yet, existing models often fail to fully leverage the non-verbal social cues human subconsciously communicate when Dec 26, 2023 · With the continuous development of deep learning, the dominant models adopted in human trajectory prediction research have changed from early manual kinematic models [2, 3] to data-driven deep network models [4, 6]. LSTM network [23] is a class of recurrent neural networks (RNN). Feb 6, 2024 · In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. In recent years, the progress of the trajectory space for human motion prediction has aroused researchers We observe that the human trajectory is not only forward predictable, but also backward predictable. 2023. Oct 22, 2022 · 2. In this work, we present Predicted Sep 10, 2024 · 这是一篇ECCV 2020 行人轨迹预测的文章，在这里对论文进行浅浅的翻译。当然，由于水平的局限，有些地方只能意译。 论文链接：Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction Abstract 理解人群动态运动对真实世界的一些应用，例如监控系统、自动驾驶来说是非常重要的。 Oct 1, 2024 · Adaptive Human Trajectory Prediction via Latent Corridors NeerjaThakkar 1,KarttikeyaMangalam ,AndreaBajcsy2,andJitendraMalik 1 UCBerkeley 2 CarnegieMellonUniversity Abstract. Crossref. However, trajectory prediction only focuses on pre-dicting a future location based on historical information. : Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction. Such algorithms have wide applications in robotics, computer vision and video Nov 15, 2018 · Given the importance of this topic, several works have been proposed for pedestrian trajectory prediction. Both forward and backward trajectories follow the same social norms and obey the same physical constraints with the only difference in their time directions. Specifically, we first get Oct 23, 2019 · In this work, we propose a Spatial-Temporal Graph Atten-tion network (STGAT), based on a sequence-to-sequence architecture to predict future trajectories of pedestrians. Besides, we show that NSP has better May 15, 2019 · This paper provides a survey of human motion trajectory prediction. there have been many approaches to attempt learning patterns of motion directly from data using a wide variety of techniques ranging from hand Dec 18, 2020 · Graph Networks for Trajectory Prediction. Sep 1, 2019 · This paper proposes a novel human trajectory prediction model in a crowded scene called the social-affinity LSTM model. However, both the social interactions among pedestrians and the intention of the Jul 1, 2021 · Human trajectory prediction is an important topic in several application domains, ranging from self-driving cars to environment design and planning, from socially-aware robots to intelligent Apr 1, 2024 · Despite the significant progress in 3D human motion prediction, the current methods, largely developed in the computer vision field, primarily focus on predicting the whole-body motion trajectory, which, while useful in certain applications, might not fully capture the nuanced motions of different body parts involved in the diverse and specialized activities within Mar 1, 2024 · Human trajectory prediction [1,2,3] aims to predict the future positions based on the previously observed positions, which is usually considered as the time series problem [4,5,6]. May 1, 2024 · We present a novel approach for long-term human trajectory prediction in indoor human-centric environments, which is essential for long-horizon robot planning in these environments. The ability to predict the future movement of humans using their past status can be used to avoid collisions and plan Aug 21, 2020 · "Social lstm: Human trajectory prediction in crowded spaces. Oct 15, 2024 · Accurate prediction of human or vehicle trajectories with good diversity that captures their stochastic nature is an essential task for many applications. Problem Settings Trajectory prediction is the problem of estimating the po- Human Trajectory Prediction is a well-studied problem with a long and rich history []. edu Abstract Pedestrians follow different trajectories to avoid obsta-cles and accommodate fellow pedestrians. Recent works based on long-short term memory ( LSTM ) models have brought tremendous improvements on the task Jul 30, 2021 · Human trajectory prediction aims at forecasting the fu-ture trajectory of pedestrians based on their past positions in complex and crowd environments. e. Accurate human trajectory prediction is crucial for applications such as autonomous vehicles, robotics, and surveillance systems. 2 seconds) i. For example, in the context of robotics, it serves as a tool for facilitating the predictions on potential future robot trajectories, useful for intelligent planning Jul 21, 2022 · Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. To leverage the potential motion patterns of human behavior, we propose a novel approach that integrates a Motion Pattern Memory with Transformer diffusion for trajectory prediction. Based on this unique property, we develop a new approach, called reciprocal learning, for human trajectory Finally, we provide code implementations of > 15 popular human trajectory forecasting baselines. Human trajectories are affected by both the physical features of the environment and social interactions with other humans. , Elhoseiny, M. May 26, 2017 · Trajectory Prediction of dynamic objects is a widely studied topic in the field of artificial intelligence. For instance, Lin, Chen, Deng, Hassan, and Fortino (2016) utilized a novel localization method (LNM) based on Markov-chain prediction and neighbor relative RSS (NRRSS), which mainly works on finger-print technology and Markov chain models for Human trajectory prediction is essential for avoiding collisions in crowded environments in the navigation of autonomous driving system. State-of-the-art human trajectory prediction methods are limited by their focus on collision avoidance and short-term planning, and their inability to model complex interactions Oct 17, 2022 · Predicting the future trajectory of a person remains a challenging problem, due to randomness and subjectivity of human movement.
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