Abstract
Aiming at the problem that the traditional kernel correlation filter (KCF) tracking algorithm cannot re-detect the target, when the target is missing due to illumination variation, severe occlusion, and out of view, this paper leads to the anomaly detection method as the target loss warning mechanism based on KCF, and at the same time, a target loss re-detection mechanism is proposed. This method detects the peak value of the response of each frame. If the abnormal peak value is found, the target is lost or will be lost. Then, the warning mechanism warns, the target template update is stopped, the target loss re-detection mechanism is started and tracks the target in full frame search. The experimental results show that the precision of the improved algorithm is 0.751, and the success rate is 0.579, which is 5.77% and 12.43% higher than that of the traditional KCF tracking algorithm, respectively. This solves the problem that the KCF tracker can recover the target to keep tracking after the target is lost, the performance of the tracking algorithm is improved, and the long-term tracking is realized.
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Liu, Yf., He, Y., Tian, Q., Yang, J. (2019). KCF Tracking Algorithm Based on Outlier Detection. In: Patnaik, S., Jain, V. (eds) Recent Developments in Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-10-8944-2_16
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DOI: https://doi.org/10.1007/978-981-10-8944-2_16
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