Approximate proximal gradient-based correlation filter for target tracking in videos: A Unified Approach

Masood, Haris, Rehman, Saad, Khan, Aimal , Riaz, Farhan, Hassan, Ali and Abbas, Muhammad (2019) Approximate proximal gradient-based correlation filter for target tracking in videos: A Unified Approach. Arabian Journal for Science and Engineering, 44 (11). pp. 9363-9380. ISSN 2191-4281

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Item Type:Article
Item Status:Live Archive


Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique.

Keywords:computer science, MACH filter, Particle filter, Approximate proximal gradient (APG), Tracking
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:52385
Deposited On:16 Nov 2022 13:44

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