Forecasting human trajectories is critical for tasks such as robot crowd navigation and autonomous driving. Modeling social interactions is of great importance for accurate group-wise motion prediction. However, most existing methods do not consider information about coherence within the crowd, but rather only pairwise interactions. In this work, we propose a novel framework, coherent motion aware graph convolutional network (CoMoGCN), for trajectory prediction in crowded scenes with group constraints. First, we cluster pedestrian trajectories into groups according to motion coherence. Then, we use graph convolutional networks to aggregate crowd information efficiently. The CoMoGCN also takes advantage of variational autoencoders to capture the multimodal nature of the human trajectories by modeling the distribution. Our method achieves state-of-the-art performance on several different trajectory prediction benchmarks, and the best average performance among all benchmarks considered.
CamDirector: Towards Long-Term Coherent Video Trajectory Editing
McMaster University（麦克 master 大学）；University of Toronto（多伦多大学）；The University of Hong Kong（香港大学）；McGill University（麦吉尔大学）；Concordia University（Concordia 大学）；MBZUAI
University of Pennsylvania（宾夕法尼亚大学）；Honda Research Institute USA（本田美国研究院）；University of Minnesota（明尼苏达大学）
Predicting an agent's future trajectory is a challenging task given the complicated stimuli (environmental/inertial/social) of motion. Prior works learn individual stimulus from different modules and fuse the representations in an end-to-end manner, which makes it hard to understand what are actually captured and how they are fused. In this work, we borrow the notion of potential field from physics as an interpretable and unified representation to model all stimuli. This allows us to not only supervise the intermediate learning process, but also have a coherent method to fuse the information o…
In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories. We present a unified model architecture for simultaneous agent future heatmap estimation, in which we leverage hierarchical and sparse image generation for fast and memory-efficient inference. We propose a learnable trajectory recombination model that takes as input a set of predicted trajectories for each agent and outputs its consistent reordered recombination. This recombination module is able to realign the initia…
Prediction with high accuracy is essential for various applications such as autonomous driving. Existing prediction models are easily prone to errors in real-world settings where observations (e.g. human poses and locations) are often noisy. To address this problem, we introduce GPRAR, a graph convolutional network based pose reconstruction and action recognition for human trajectory prediction. The key idea of GPRAR is to generate robust features: human poses and actions, under noisy scenarios. To this end, we design GPRAR using two novel sub-networks: PRAR (Pose Reconstruction and Action Rec…
Trajectory Forcing: Structure-First Generation with Controllable Semantic Trajectories
University of Tübingen（图宾根大学）；ETH Zürich（苏黎世联邦理工学院）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；ELLIS Institute（ELLIS研究所）；Tübingen AI Center（图宾根人工智能中心）
SCT-MOT: Enhancing Air-to-Air Multiple UAVs Tracking with Swarm-Coupled Motion and Trajectory Guidance
China-UAE Belt and Road Joint Laboratory on Intelligent Unmanned Systems of School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学宇航学院中国-阿联酋‘一带一路’智能无人系统联合实验室）；School of Aerospace Engineering, Beijing Institute of Technology（北京理工大学宇航学院）；School of Information and Computing Science, Xi'an Jiaotong-Liverpool University（西交利物浦大学信息与计算科学学院）
