Dense Crowd Motion Prediction through Density and Trend Maps
| dc.contributor.author | Wang, Tingting | en_US |
| dc.contributor.author | Fu, Qiang | en_US |
| dc.contributor.author | Wang, Minggang | en_US |
| dc.contributor.author | Bi, Huikun | en_US |
| dc.contributor.author | Deng, Qixin | en_US |
| dc.contributor.author | Deng, Zhigang | en_US |
| dc.contributor.editor | Chen, Renjie | en_US |
| dc.contributor.editor | Ritschel, Tobias | en_US |
| dc.contributor.editor | Whiting, Emily | en_US |
| dc.date.accessioned | 2024-10-13T18:04:24Z | |
| dc.date.available | 2024-10-13T18:04:24Z | |
| dc.date.issued | 2024 | |
| dc.description.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. | en_US |
| dc.description.sectionheaders | Crowd and Scene Analysis | |
| dc.description.seriesinformation | Pacific Graphics Conference Papers and Posters | |
| dc.identifier.doi | 10.2312/pg.20241295 | |
| dc.identifier.isbn | 978-3-03868-250-9 | |
| dc.identifier.pages | 9 pages | |
| dc.identifier.uri | https://doi.org/10.2312/pg.20241295 | |
| dc.identifier.uri | https://diglib.eg.org/handle/10.2312/pg20241295 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies → Neural networks; Tracking | |
| dc.subject | Computing methodologies → Neural networks | |
| dc.subject | Tracking | |
| dc.title | Dense Crowd Motion Prediction through Density and Trend Maps | en_US |