Abstract
Dense areas of pedestrians in complex crowded scenes tend to disrupt the proper path of the agents. The agents usually avoid gathering areas to find a reasonable pedestrian-sparse path, slow down the speed to walk, and wait for the gathering pedestrians to disperse. The accurate trajectory prediction in gathering areas is a challenging problem. This work introduces a new feature that affects trajectories to address this problem. The area gathering feature that allows agents to plan future paths based on the gathering level of pedestrians. The gathering areas as well as indicate the degree of gathering in the areas by means of a dynamic pedestrian filtering method to generate a trajectory heat map. Besides, the convolutional neural network is used to extract the corresponding area gathering feature. Furthermore, a new approach is proposed for inter-agent interactions that makes full excavation of deep interaction information and takes into account a more comprehensive interaction behavior. This work predicts trajectories by incorporating multiple factors such as area-dense features, social interactions, scene context, and individual intent. The prediction accuracy is significantly enhanced and outperforms state-of-the-art methods.
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National Natural Science Foundation of China under Grant No. 62106117, and Shandong Provincial Natural Science Foundation under Grant No. ZR2021QF084.
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Ye, R., Lv, Z., Zhao, A., Li, J. (2022). Socially Acceptable Trajectory Prediction for Scene Pedestrian Gathering Area. In: Wang, L., Segal, M., Chen, J., Qiu, T. (eds) Wireless Algorithms, Systems, and Applications. WASA 2022. Lecture Notes in Computer Science, vol 13471. Springer, Cham. https://doi.org/10.1007/978-3-031-19208-1_18
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