#239: CS-LOFT: Color and scale adaptive tracking using Bhattacharyya distance and max pooling consistency

N. Al-Shakarji, F. Bunyak, and K. Palaniappan

IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2016

color, tracking, features, single object tracking

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Robust and accurate visual tracking is needed for many computer vision applications from video summarization to visual surveillance. Visual tracking remains to be a challenging task because of factors such as changing object appearance, illumination variations and shadows, partial and full occlusions, camera motion, distractors, and scale changes. Recently our group proposed a Likelihood of Features Tracking (LoFT) system that fuses multiple sources of information about target and its environment to perform robust single object tracking. LoFT has been shown to successfully track objects under different scenarios from full motion video to wide-are motion imagery. In this paper, we extend the LoFT framework with color information and multi-scale feature sets to boost its performance. Color information is incorporated through Color Name (CN) mapping scheme.Multiscale selective has been done according to select the suitable scale among the variation of scale set. Experiments on VOT 2015 tracking benchmark, which includes video sequences with significant color and scale variations, show significant performance improvement over baseline LoFT both in accuracy and robustness 11% and 24% respectively.