Probabilistic fusion-based parameter estimation for visual tracking
Computer Vision and Image Understanding
Recent advances and trends in visual tracking: A review
Neurocomputing
Structured visual tracking with dynamic graph
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Tracking in dense crowds using prominence and neighborhood motion concurrence
Image and Vision Computing
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Many tracking methods face a fundamental dilemma in practice: tracking has to be computationally efficient but verifying if or not the tracker is following the true target tends to be demanding, especially when the background is cluttered and/or when occlusion occurs. Due to the lack of a good solution to this problem, many existing methods tend to be either computationally intensive with the use of sophisticated image observation models, or vulnerable to the false alarms. This greatly threatens long-duration robust tracking. This paper presents a novel solution to this dilemma by integrating into the tracking process a set of auxiliary objects that are automatically discovered in the video on the fly by data mining. Auxiliary objects have three properties at least in a short time interval: (1) persistent co-occurrence with the target; (2) consistent motion correlation with the target; and (3) easy to track. The collaborative tracking of these auxiliary objects leads to an efficient computation as well as a strong verification. Our extensive experiments have exhibited exciting performance in very challenging real-world testing cases.