Online SVM and backward model validation based visual tracking
Dhruv Mullick, A. Venkata Subramanyam, Sabu Emmanuel · 2017
Visual object tracking involves the challenging task of scale adaptation to the changing object appearance. Sometimes, this leads to excessive expansion or contraction of the estimated bounding box. Towards this, several generative, discriminatory and hybrid models have been proposed. In this paper, we propose to apply Backward Validation Tracking (BVT) along with an online SVM. BVT has an advantage that it creates a model pool depending on the amount of variation of object's appearance in subsequent frames. Thus while tracking an object, not only is the current appearance taken into account, but so are the previous appearances which are stored in the model pool. Further, in order to improve on the appearance model adaptation, we use an online SVM. The online SVM is a discriminatory algorithm which allows us to distinguish between foreground and background objects. On account of its online nature, the SVM adapts to the updates in the appearance of the target object. We perform extensive experiments on the Object Tracking Benchmark (OTB) dataset. Experimental results prove that the proposed tracker outperforms several popular trackers, in terms of both overlap ratio and precision.