An Improved Tracking-Learning-Detection Algorithm for Low Frame Rate
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
The conventional Tracking-Learning-Detection (TLD) algorithm is sensitive to illumination change and clutter and low frame rate and results in drift even missing. To overcome these shortcomings and increase robustness, by improving the TLD structure via integrating mean-shift and co-training learning can be achieved better results undergo low frame rate (LFR) condition and the robustness and accuracy tracking of the TLD structure increases. Because of, the Mean-Shift tracking algorithm is robust to rotation, partial occlusion and scale changing and it is simple to implement and takes less computational time. On the other, the co-training learning algorithm with two independent classifiers can learn changes of the target features in during the online tracking process. Therefore, the extended structure can solve the problem of lost object tracking in LFR videos and other challenges simultaneously. Finally, comparative evaluations of the proposed method to other top state-of-the-art tracking algorithms under the various scenarios from the TB-100 known dataset, demonstrate the superior performance of the proposed algorithm compared to other tracking algorithms in terms of tracking robustness and stability performance. Finally, the proposed structure based on the TLD architecture, in scenarios with the various challenges mentioned, will improve on average about 33% of the results, compared to the traditional TLD algorithm.
Keywords:
Language:
Persian
Published:
Journal of Intelligent Procedures in Electrical Technology, Volume:14 Issue: 54, 2022
Pages:
121 to 134
https://www.magiran.com/p2445261
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