Tracking Large Variable Numbers of Objects in Clutter

We propose statistical data association techniques for visual tracking of enormously large numbers of objects. We do not assume any prior knowledge about the numbers involved, and the objects may appear or disappear anywhere in the image frame and at any time in the sequence. Our approach combines the techniques of multitarget track initiation, recursive Bayesian tracking, clutter modeling, event analysis, and multiple hypothesis filtering. The original multiple hypothesis filter addresses an NP-hard problem and is thus not practical. We propose two cluster-based data association approaches that are linear in the number of detections and tracked objects. We applied the method to track wildlife in infrared video. We have successfully tracked hundreds of thousands of bats which were flying at high speeds and in dense formations.

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Margrit Betke, Diane E. Hirsh, Angshuman Bagchi, Nickolay I. Hristov, Nicholas C. Makris, Thomas H. Kunz
• Keywords: Cluster-based Data Association, Computer Vision, CVPR 2007, Data Association Techniques, Multiple Hypothesis Filter, Multitarget Track Initiation, Recursive Bayesian Tracking