Visual Tracking Using Stereo Images

Abstract: Visual tracking concerns the problem of following an arbitrary object in a video sequence. In this thesis, we examine how to use stereo images to extend existing visual tracking algorithms, which methods exists to obtain information from stereo images, and how the results change as the parameters to each tracker vary. For this purpose, four abstract approaches are identified, with five distinct implementations. Each tracker implementation is an extension of a baseline algorithm, MOSSE. The free parameters of each model are optimized with respect to two different evaluation strategies called nor- and wir-tests, and four different objective functions, which are then fixed when comparing the models against each other. The results are created on single target tracks extracted from the KITTI tracking dataset, and the optimization results show that none of the objective functions are sensitive to the exposed parameters under the joint selection of model and dataset. The evaluation results also shows that none of the extensions improve the results of the baseline tracker.