Tracking objects with co-occurrence matrix and particle filter in infrared video sequences

Tracking objects with co-occurrence matrix and particle filter in infrared video sequences

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Tracking objects in infrared video sequences became a very important challenge for many current tracking algorithms due to several complex situations such as illumination variation, night vision, and occlusion. This study proposes a new tracker that uses a set of invariant parameters calculated via the co-occurrence moments to better describe the target object. The usage of the co-occurrence moments gives the ability to exploit the information about the texture of the target to enhance the robustness of the tracking task. This latter is performed without any learning or clustering phase. The qualitative and quantitative studies on challenging sequences demonstrate that the results obtained by the proposed algorithm are very competitive in comparison to several state-of-the-art methods.


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