Window Based Min-Max Feature Extraction for Visual Object Tracking

Rubeena Banu; M H Sidram

doi:10.17485/IJST/v15i40.1395

Article

Window Based Min-Max Feature Extraction for Visual Object Tracking

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v15i40.1395

Year: 2022, Volume: 15, Issue: 40, Pages: 2047-2055

Original Article

Window Based Min-Max Feature Extraction for Visual Object Tracking

Rubeena Banu^1*, M H Sidram²

¹Research Scholar, Department of Electrical and Electronics Engineering, Sri Jayachamarajendra College of Engineering, VTU University, Mysuru-570006, Karnataka, India
²Associate Professor, Department of Electrical and Electronics Engineering, Sri Jayachamarajendra College of Engineering, VTU University, Mysuru-570006, Karnataka, India

*Corresponding Author
Email: [email protected]

Received Date:06 July 2022, Accepted Date:12 September 2022, Published Date:18 October 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Background/Objectives: Visual object tracking is considered as difficult problem and becomes more challenging because of the various environmental conditions. In order to achieve the efficient result in the area of visual tracking, feature extraction will play the important role. This study demonstrates the Min-max feature extraction method to improve the tracker robustness in the area of visual tracking. Methods: The proposed min-max features along with existing Histogram of Oriented Gradient (HOG), and Convolution Neural Network (CNN) features are given to the Spatial Temporal Regularized Correlation Filter (STRCF) to find the new position of the target and it is successfully solved through Alternative Direction Method of Multipliers (ADMM). By employing both Spatial and temporal regularization methods, without much compromise in the efficiency, the boundary effect is handled. The min-max feature will extract the object’s window-based features as foreground and background. The foreground consists of higher color values than the background. As compared to the Color Names (CN) proposed minmax feature method gives accurate features to identify the objects in a video. In order to present the performance, the method is tested on the OTB dataset image sequences and compared with the state-of-the-art tracker and achieved the promising results for all of tested videos. Findings: Our method, using Minmax feature, gives Mean OP and FPS 61.44% &18.27 respectively, which shows improvement in the tracking accuracy along with the computational speed as compared to CN feature.

Keywords: MinMax feature; CNN; Histogram of Oriented Gradient; Occlusion; Visual tracking; Video surveillance

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Copyright

© 2022 Banu & Sidram. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)