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Online, Real-Time Tracking Using a Category-to-Individual Detector***
David Hall and Pietro Perona
California Institute of Technology, USAdhall@vision.caltech.edu
perona@vision.caltech.edu
Abstract. A method for online, real-time tracking of objects is presented. Tracking is treated as a repeated detection problem where potential target objects are identified with a pre-trained category detector and object identity across frames is established by individual-specific detectors. The individual detectors are (re-)trained online from a single positive example whenever there is a coincident category detection. This ensures that the tracker is robust to drift. Real-time operation is possible since an individual-object detector is obtained through elementary manipulations of the thresholds of the category detector and therefore only minimal additional computations are required. Our tracking algorithm is benchmarked against nine state-of-the-art trackers on two large, publicly available and challenging video datasets. We find that our algorithm is 10% more accurate and nearly as fast as the fastest of the competing algorithms, and it is as accurate but 20 times faster than the most accurate of the competing algorithms.
Electronic Supplementary Material:
*Project Website: http://vision.caltech.edu/~dhall/projects/CIT/
LNCS 8689, p. 361 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014