Fast Tracking via Dense Spatio-Temporal Context Learning

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Authors Kaihua Zhang, Lei Zhang, Qingshan Liu, David Zhang and Ming-Hsuan Yang
Journal/Conference Name European Conference on Computer Vision 2014
Paper Category
Paper Abstract In this paper, we present a simple yet fast and robust algorithm which exploits the dense spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its locally dense contexts in a Bayesian framework, which models the statistical correlation between the simple low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is then posed by computing a confidence map which takes into account the prior information of the target location and thereby alleviates target location ambiguity effectively. We further propose a novel explicit scale adaptation scheme, which is able to deal with target scale variations efficiently and effectively. The Fast Fourier Trans-form (FFT) is adopted for fast learning and detection in this work, which onlyneeds4FFT operations. Implemented in MATLAB without code optimization, the proposed tracker runs at350frames per second on an i7machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.
Date of publication 2014
Code Programming Language MATLAB

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