AdaPT: Real-time adaptive pedestrian tracking for crowded scenes
Aniket Bera, Nico Galoppo, Dillon Sharlet, Adam Lake, Dinesh Manocha · 2014
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.