An Improved Tracking-Learning-Detection Algorithm for Low Frame Rate

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Moridvaisi Hooman; RAZZAZI FARBOD; POURMINA MOHAMMAD ALI; Dousti Massoud

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Journal Paper

Paper Information

Journal: Journal of Intelligent Procedures in Electrical Technology Year:2023 | Volume:14 | Issue:54 Page(s): 121-134

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Information Journal Paper

Title

An Improved Tracking-Learning-Detection Algorithm for Low Frame Rate

Author(s)

Moridvaisi Hooman | RAZZAZI FARBOD | POURMINA MOHAMMAD ALI | Dousti Massoud | Issue Writer Certificate

Keywords

low frame rate

machine learning algorithm

mean-shift algorithm

target tracking

tracking learning detection

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.

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APA: Copy

Moridvaisi, Hooman, RAZZAZI, FARBOD, POURMINA, MOHAMMAD ALI, & Dousti, Massoud. (2023). An Improved Tracking-Learning-Detection Algorithm for Low Frame Rate. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, 14(54 ), 121-134. SID. https://sid.ir/paper/1034884/en

Vancouver: Copy

Moridvaisi Hooman, RAZZAZI FARBOD, POURMINA MOHAMMAD ALI, Dousti Massoud. An Improved Tracking-Learning-Detection Algorithm for Low Frame Rate. JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY[Internet]. 2023;14(54 ):121-134. Available from: https://sid.ir/paper/1034884/en

IEEE: Copy

Hooman Moridvaisi, FARBOD RAZZAZI, MOHAMMAD ALI POURMINA, and Massoud Dousti, “An Improved Tracking-Learning-Detection Algorithm for Low Frame Rate,” JOURNAL OF INTELLIGENT PROCEDURES IN ELECTRICAL TECHNOLOGY, vol. 14, no. 54 , pp. 121–134, 2023, [Online]. Available: https://sid.ir/paper/1034884/en

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