Dense Crowd Motion Prediction through Density and Trend Maps
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
In this paper we propose a novel density/trend map based method to predict both group behavior and individual pedestrian motion from video input. Existing motion prediction methods represent pedestrian motion as a set of spatial-temporal trajectories; however, besides such a per-pedestrian representation, a high-level representation for crowd motion is often needed in many crowd applications. Our method leverages density maps and trend maps to represent the spatial-temporal states of dense crowds. Based on such representations, we propose a crowd density map net that extracts a density map from a video clip, and a crowd prediction net that utilizes the historical states of a video clip to predict density maps and trend maps for future frames. Moreover, since the crowd motion consists of the motion of individual pedestrians in a group, we also leverage the predicted crowd motion as a clue to improve the accuracy of traditional trajectory-based motion prediction methods. Through a series of experiments and comparisons with state-of-the-art motion prediction methods, we demonstrate the effectiveness and robustness of our method.
Description
CCS Concepts: Computing methodologies → Neural networks; Tracking
@inproceedings{10.2312:pg.20241295,
booktitle = {Pacific Graphics Conference Papers and Posters},
editor = {Chen, Renjie and Ritschel, Tobias and Whiting, Emily},
title = {{Dense Crowd Motion Prediction through Density and Trend Maps}},
author = {Wang, Tingting and Fu, Qiang and Wang, Minggang and Bi, Huikun and Deng, Qixin and Deng, Zhigang},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-250-9},
DOI = {10.2312/pg.20241295}
}