We present a novel realtime algorithm to compute the trajectory of each pedestrian in a crowded scene. Our formulation is based on an adaptive scheme that uses a combination of deterministic and probabilistic trackers to achieve high accuracy and efficiency simultaneously. Furthermore, we integrate it with a multi-agent motion model and local interaction scheme to accurately compute the trajectory of each pedestrian. We highlight the performance and benefits of our algorithm on well-known datasets with tens of pedestrians.
Results
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Publication
"AdaPT: Real-time Adaptive Pedestrian Tracking for crowded scenes" [PDF]
Aniket Bera, Nico Galoppo, Dillon Sharlet, Adam Lake, Dinesh Manocha
2014 IEEE International Conference on Robotics and Automation (ICRA 2014) [Accepted]
Aniket Bera, Nico Galoppo, Dillon Sharlet, Adam Lake, Dinesh Manocha
2014 IEEE International Conference on Robotics and Automation (ICRA 2014) [Accepted]