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Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking

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Abstract

Correlation tracker is computation intensive (if the search space or the template is large), has template drift problem, and may fail in case of fast maneuvering target, rapid changes in its appearance, occlusion suffered by it and clutter in the scene. Kalman filter can predict the target coordinates in the next frame, if the measurement vector is supplied to it by a correlation tracker. Thus, a relatively small search space can be determined where the probability of finding the target in the next frame is high. This way, the tracker can become fast and reject the clutter, which is outside the search space in the scene. However, if the tracker provides wrong measurement vector due to the clutter or the occlusion inside the search region, the efficacy of the filter is significantly deteriorated. Mean-shift tracker is fast and has shown good tracking results in the literature, but it may fail when the histograms of the target and the candidate region in the scene are similar (even when their appearance is different). In order to make the overall visual tracking framework robust to the mentioned problems, we propose to combine the three approaches heuristically, so that they may support each other for better tracking results. Furthermore, we present novel method for (1) appearance model updating which adapts the template according to rate of appearance change of target, (2) adaptive threshold for similarity measure which uses the variable threshold for each forthcoming image frame based on current frame peak similarity value, and (3) adaptive kernel size for fast mean-shift algorithm based on varying size of the target. Comparison with nine state-of-the-art tracking algorithms on eleven publically available standard dataset shows that the proposed algorithm outperforms the other algorithms in most of the cases.

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Acknowledgments

This research work is supported by PIEAS-administered HEC Endowment Fund for Higher education and R&D for IT and Telecom Sector Fund.

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Authors and Affiliations

  1. Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan

    Ahmad Ali, Abdul Jalil, Muhammad Aksam Iftikhar & Mutawarra Hussain

  2. NUST Military College of Signals, Islamabad, Pakistan

    Javed Ahmed

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  1. Ahmad Ali
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Ali, A., Jalil, A., Ahmed, J. et al. Correlation, Kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking. SIViP 9, 1567–1585 (2015). https://doi.org/10.1007/s11760-014-0612-0

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