Issue No. 05 - May (2012 vol. 34)
L. Kratz , Dept. of Comput. Sci., Drexel Univ., Philadelphia, PA, USA
K. Nishino , Dept. of Comput. Sci., Drexel Univ., Philadelphia, PA, USA
Tracking pedestrians is a vital component of many computer vision applications, including surveillance, scene understanding, and behavior analysis. Videos of crowded scenes present significant challenges to tracking due to the large number of pedestrians and the frequent partial occlusions that they produce. The movement of each pedestrian, however, contributes to the overall crowd motion (i.e., the collective motions of the scene's constituents over the entire video) that exhibits an underlying spatially and temporally varying structured pattern. In this paper, we present a novel Bayesian framework for tracking pedestrians in videos of crowded scenes using a space-time model of the crowd motion. We represent the crowd motion with a collection of hidden Markov models trained on local spatio-temporal motion patterns, i.e., the motion patterns exhibited by pedestrians as they move through local space-time regions of the video. Using this unique representation, we predict the next local spatio-temporal motion pattern a tracked pedestrian will exhibit based on the observed frames of the video. We then use this prediction as a prior for tracking the movement of an individual in videos of extremely crowded scenes. We show that our approach of leveraging the crowd motion enables tracking in videos of complex scenes that present unique difficulty to other approaches.
spatiotemporal phenomena, computer vision, hidden Markov models, pedestrians, hidden Markov models, pedestrians tracking, spatio temporal motion patterns, crowded scenes, computer vision, occlusions, crowd motion, Bayesian framework, space time model, Videos, Hidden Markov models, Target tracking, Training, Trajectory, Dynamics, hidden Markov models., Tracking, video analysis, crowded scenes, spatio-temporal motion patterns
L. Kratz and K. Nishino, "Tracking Pedestrians Using Local Spatio-Temporal Motion Patterns in Extremely Crowded Scenes," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 987-1002, 2012.