1,560 research outputs found

    Human Motion Trajectory Prediction: A Survey

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    With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. 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. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

    Generator with Triangulation for Pedestrians Trajectory Prediction

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    Pedestrian trajectory prediction is a basic task in computer vision field. The prosperity of artificial intelligence makes the automatic drive, human-robot interaction and surveillance video attract a great deal of attention. Generally, researchers always place emphasis on pedestrian trajectory. The focuses of pedestrian trajectory prediction task are motion pattern modelling and spatio-temporal interaction modelling in the current study. In our paper, we present a GAN-based framework to model pedestrian motion pattern. A Delaunay triangulation algorithm is applied to map the pedestrian interaction. From the perspective of space, both the position interaction and motion interaction of pedestrians can be considered. For example, the influence of the movement direction and motion potential energy of pedestrians on the surrounding pedestrians can be modelled

    Forecasting People Trajectories and Head Poses by Jointly Reasoning on Tracklets and Vislets

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    In this work, we explore the correlation between people trajectories and their head orientations. We argue that people trajectory and head pose forecasting can be modelled as a joint problem. Recent approaches on trajectory forecasting leverage short-term trajectories (aka tracklets) of pedestrians to predict their future paths. In addition, sociological cues, such as expected destination or pedestrian interaction, are often combined with tracklets. In this paper, we propose MiXing-LSTM (MX-LSTM) to capture the interplay between positions and head orientations (vislets) thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. We additionally exploit the head orientations as a proxy for the visual attention, when modeling social interactions. MX-LSTM predicts future pedestrians location and head pose, increasing the standard capabilities of the current approaches on long-term trajectory forecasting. Compared to the state-of-the-art, our approach shows better performances on an extensive set of public benchmarks. MX-LSTM is particularly effective when people move slowly, i.e. the most challenging scenario for all other models. The proposed approach also allows for accurate predictions on a longer time horizon.Comment: Accepted at IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019. arXiv admin note: text overlap with arXiv:1805.0065
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