Detecting motion synchrony by video tubes
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Online spatio-temporal structural context learning for visual tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
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We present a new paradigm for tracking objects in video in the presence of other similar objects. This branch-and-track paradigm is also useful in the absence of motion, for the discovery of repetitive patterns in images. The object of interest is the lead object and the distracters are extras. The lead tracker branches out trackers for extras when they are detected, and all trackers share a common set of features. Sometimes, extras are tracked because they are of interest in their own right. In other cases, and perhaps more importantly, tracking extras makes tracking the lead nimbler and more robust, both because shared features provide a richer object model, and because tracking extras accounts for sources of confusion explicitly. Sharing features also makes joint tracking less expensive, and coordinating tracking across lead and extras allows optimizing window positions jointly rather than separately, for better results. The joint tracking of both lead and extras can be solved optimally by dynamic programming and branching is quickly determined by efficient subwindow search. Matlab experiments show near real time performance at 5-30 frames per second on a single-core laptop for 240 by 320 images.