You'll Never Walk Alone: Modeling Social Behavior for Multi-Target Tracking. Pellegrini, S, Ess, A, Schindler, K, & Van Gool, L In 2009 IEEE 12th International Conference on Computer Vision, pages 261–268, Kyoto, September, 2009. IEEE. doi abstract bibtex Object tracking typically relies on a dynamic model to predict the object's location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions.
@inproceedings{pellegrini2009you,
title = {You'll Never Walk Alone: {{Modeling}} Social Behavior for Multi-Target Tracking},
shorttitle = {You'll Never Walk Alone},
booktitle = {2009 {{IEEE}} 12th {{International Conference}} on {{Computer Vision}}},
author = {Pellegrini, S and Ess, A and Schindler, K and Van Gool, L},
year = {2009},
month = sep,
pages = {261--268},
publisher = {IEEE},
address = {Kyoto},
doi = {10.1109/ICCV.2009.5459260},
urldate = {2024-05-11},
abstract = {Object tracking typically relies on a dynamic model to predict the object's location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions.},
isbn = {978-1-4244-4420-5},
langid = {english},
keywords = {Dataset: self,VRUs path},
file = {C:\Users\gregf\Zotero\storage\UVESI6MZ\Pellegrini et al. - 2009 - You'll never walk alone Modeling social behavior .pdf}
}
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