It remains challenging to automatically predict the multi-agent trajectory due to multiple interactions including agent to agent interaction and scene to agent interaction. Although recent methods have achieved promising performance, most of them just consider spatial influence of the interactions and ignore the fact that temporal influence always accompanies spatial influence. Moreover, those methods based on scene information always require extra segmented scene images to generate multiple socially acceptable trajectories. To solve these limitations, we propose a novel model named spatial-temporal attentive network with spatial continuity (STAN-SC). First, spatial-temporal attention mechanism is presented to explore the most useful and important information. Second, we conduct a joint feature sequence based on the sequence and instant state information to make the generative trajectories keep spatial continuity. Experiments are performed on the two widely used ETH-UCY datasets and demonstrate that the proposed model achieves state-of-the-art prediction accuracy and handles more complex scenarios.
Stanford University（斯坦福大学）；Race Optimal（Race Optimal公司）；Ecole Polytechnique Federale de Lausanne (EPFL)（洛桑联邦理工学院（EPFL））
We present an interpretable framework for path prediction that leverages dependencies between agents' behaviors and their spatial navigation environment. We exploit two sources of information: the past motion trajectory of the agent of interest and a wide top-view image of the navigation scene. We propose a Clairvoyant Attentive Recurrent Network (CAR-Net) that learns where to look in a large image of the scene when solving the path prediction task. Our method can attend to any area, or combination of areas, within the raw image (e.g., road intersections) when predicting the trajectory of the …
Leibniz University Hannover（莱布尼茨汉诺威大学）；University of Twente（特文特大学）
Trajectory prediction is critical for applications of planning safe future movements and remains challenging even for the next few seconds in urban mixed traffic. How an agent moves is affected by the various behaviors of its neighboring agents in different environments. To predict movements, we propose an end-to-end generative model named Attentive Maps Encoder Network (AMENet) that encodes the agent's motion and interaction information for accurate and realistic multi-path trajectory prediction. A conditional variational auto-encoder module is trained to learn the latent space of possible fu…
Carnegie Mellon University（卡内基梅隆大学）；Bosch Research Pittsburgh（博世匹兹堡研究院）
Effective feature-extraction is critical to models' contextual understanding, particularly for applications to robotics and autonomous driving, such as multimodal trajectory prediction. However, state-of-the-art generative methods face limitations in representing the scene context, leading to predictions of inadmissible futures. We alleviate these limitations through the use of self-attention, which enables better control over representing the agent's social context; we propose a local feature-extraction pipeline that produces more salient information downstream, with improved parameter effici…
Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）；Providence University（静宜大学）
Learning to capture human motion is essential to 3D human pose and shape estimation from monocular video. However, the existing methods mainly rely on recurrent or convolutional operation to model such temporal information, which limits the ability to capture non-local context relations of human motion. To address this problem, we propose a motion pose and shape network (MPS-Net) to effectively capture humans in motion to estimate accurate and temporally coherent 3D human pose and shape from a video. Specifically, we first propose a motion continuity attention (MoCA) module that leverages visu…
Universitá degli Studi di Trento（特伦托大学）；Universitá degli Studi di Modena e Reggio Emilia（摩德纳-雷焦艾米利亚大学）；Universitá degli Studi di Padova（帕多瓦大学）
Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented by the inherently multi-modal nature of human paths which makes socially acceptable multiple futures when human interactions are involved. To this end, we propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning mainly relies on prior belief maps, representing most likely moving…
AImageLab（AImage实验室）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment. This is non-trivial because human motion is inherently multi-modal: given a history of human motion paths, there are many plausible ways by which people could move in the future. Additionally, people activities are often driven by goals, e.g. reaching particular locations or interacting with the environment. We address the aforementioned aspects by pr…
University of Padova（帕多瓦大学）；University of Modena and Reggio Emilia（摩德纳-雷焦艾米利亚大学）
Human trajectory forecasting is a key component of autonomous vehicles, social-aware robots and advanced video-surveillance applications. This challenging task typically requires knowledge about past motion, the environment and likely destination areas. In this context, multi-modality is a fundamental aspect and its effective modeling can be beneficial to any architecture. Inferring accurate trajectories is nevertheless challenging, due to the inherently uncertain nature of the future. To overcome these difficulties, recent models use different inputs and propose to model human intentions usin…
Stanford University（斯坦福大学）；University of Florida（佛罗里达大学）；University of Adelaide（阿德莱德大学）
This paper addresses the problem of path prediction for multiple interacting agents in a scene, which is a crucial step for many autonomous platforms such as self-driving cars and social robots. We present \textit{SoPhie}; an interpretable framework based on Generative Adversarial Network (GAN), which leverages two sources of information, the path history of all the agents in a scene, and the scene context information, using images of the scene. To predict a future path for an agent, both physical and social information must be leveraged. Previous work has not been successful to jointly model …
University of Virginia（弗吉尼亚大学）；Iowa State University（爱荷华州立大学）
Safe navigation of autonomous agents in human centric environments requires the ability to understand and predict motion of neighboring pedestrians. However, predicting pedestrian intent is a complex problem. Pedestrian motion is governed by complex social navigation norms, is dependent on neighbors' trajectories, and is multimodal in nature. In this work, we propose SCAN, a Spatial Context Attentive Network that can jointly predict socially-acceptable multiple future trajectories for all pedestrians in a scene. SCAN encodes the influence of spatially close neighbors using a novel spatial atte…
Fine-Grained Behavior and Lane Constraints Guided Trajectory Prediction Method
School of Mechanical Engineering, Zhejiang University（浙江大学机械工程学院）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院广东省全驱系统控制理论与技术重点实验室）；Leapmotor Technology（零跑科技）
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校）；FHWA Center of Excellence on New Mobility and Automated Vehicles（联邦公路管理局新 mobility 与自动驾驶汽车卓越中心）
Huazhong University of Science and Technology（华中科技大学）；Huawei Technologies Co., Ltd.（华为技术有限公司）
Text tracking is to track multiple texts in a video,and construct a trajectory for each text. Existing methodstackle this task by utilizing the tracking-by-detection frame-work, i.e., detecting the text instances in each frame andassociating the corresponding text instances in consecutiveframes. We argue that the tracking accuracy of this paradigmis severely limited in more complex scenarios, e.g., owing tomotion blur, etc., the missed detection of text instances causesthe break of the text trajectory. In addition, different textinstances with similar appearance are easily confused, leadingto …
TrajGATFormer: A Graph-Based Transformer Approach for Worker and Obstacle Trajectory Prediction in Off-site Construction Environments
organization= Department of Civil \& Environmental Engineering, University of Alberta , addressline= , city= Edmonton , postcode= T6G 2R3 , state= AB , country= Canada；organization= Department of Mechanical Engineering, University of Alberta , addressline= , city= Edmonton , postcode= T6G 2R3 , state= AB , country= Canada
CylindTrack: Depth-Aware Cylindrical Motion Modeling for Panoramic Multi-Object Tracking
School of Artificial Intelligence and Robotics and the National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, China（人工智能与机器人学院及机器人视觉感知与控制技术国家工程研究中心，湖南大学，中国）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University, China（先进技术学院，西安交通大学利物浦大学，中国）；Suzhou VSDeep Intelligent Technology Co., Ltd., China（苏州VSDeep智能科技有限公司，中国）；BWTON Technology Co., Ltd., China（BWTON科技有限公司，中国）
