Open Access

ARTICLE

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:

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

Received 18 September 2021; Accepted 14 December 2021; Issue published 01 June 2022

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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Keywords

Object tracking; image processing; background learning; graph embedding algorithm; computer vision

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