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Multiple Object Tracking through Background Learning

Deependra Sharma*, Zainul Abdin Jaffery

Jamia Millia Islamia, Department of Electrical Engineering, New-Delhi, 110025, India

* Corresponding Author: Deependra Sharma. Email: email

Computer Systems Science and Engineering 2023, 44(1), 191-204. https://doi.org/10.32604/csse.2023.023728

Abstract

This paper discusses about the new approach of multiple object tracking relative to background information. The concept of multiple object tracking through background learning is based upon the theory of relativity, that involves a frame of reference in spatial domain to localize and/or track any object. The field of multiple object tracking has seen a lot of research, but researchers have considered the background as redundant. However, in object tracking, the background plays a vital role and leads to definite improvement in the overall process of tracking. In the present work an algorithm is proposed for the multiple object tracking through background learning. The learning framework is based on graph embedding approach for localizing multiple objects. The graph utilizes the inherent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects. The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures. It is observed that our proposed algorithm gives better performance.

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Cite This Article

APA Style
Sharma, D., Jaffery, Z.A. (2023). Multiple object tracking through background learning. Computer Systems Science and Engineering, 44(1), 191-204. https://doi.org/10.32604/csse.2023.023728
Vancouver Style
Sharma D, Jaffery ZA. Multiple object tracking through background learning. Comput Syst Sci Eng. 2023;44(1):191-204 https://doi.org/10.32604/csse.2023.023728
IEEE Style
D. Sharma and Z.A. Jaffery, "Multiple Object Tracking through Background Learning," Comput. Syst. Sci. Eng., vol. 44, no. 1, pp. 191-204. 2023. https://doi.org/10.32604/csse.2023.023728



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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