Abstract—Support Vector Tracking (SVT) integrates the Support Vector Machine (SVM) classifier into an optic-flow-based tracker. Instead of minimizing an intensity difference function between successive frames, SVT maximizes the SVM classification score. To account for large motions between successive frames, we build pyramids from the support vectors and use a coarse-to-fine approach in the classification stage. We show results of using SVT for vehicle tracking in image sequences.
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Index Terms:
Support vector machines, optic-flow, visual tracking.
Citation:
Shai Avidan, "Support Vector Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 8, pp. 1064-1072, Aug. 2004, doi:10.1109/TPAMI.2004.53
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