IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic data association methods in visual tracking of groups
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A Multiple-Hypothesis Approach for Multiobject Visual Tracking
IEEE Transactions on Image Processing
Multiclass Multimodal Detection and Tracking in Urban Environments
International Journal of Robotics Research
Robust hierarchical multiple hypothesis tracker for multiple-object tracking
Expert Systems with Applications: An International Journal
Tracking vehicles as groups in airborne videos
Neurocomputing
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People in densely populated environments typically form groups that split and merge. In this paper we track groups of people so as to reflect this formation process and gain efficiency in situations where maintaining the state of individual people would be intractable. We pose the group tracking problem as a recursive multi-hypothesis model selection problem in which we hypothesize over both, the partitioning of tracks into groups (models) and the association of observations to tracks (assignments). Model hypotheses that include split, merge, and continuation events are first generated in a data-driven manner and then validated by means of the assignment probabilities conditioned on the respective model. Observations are found by clustering points from a laser range finder given a background model and associated to existing group tracks using the minimum average Hausdorff distance. Experiments with a stationary and a moving platform show that, in populated environments, tracking groups is clearly more efficient than tracking people separately. Our system runs in real-time on a typical desktop computer.