Performance Evaluation of Background Subtraction Techniques for Video Frames

Qasim, Salman, Khan, Kaleem, Yu, Miao and Khan, Muhammad (2021) Performance Evaluation of Background Subtraction Techniques for Video Frames. In: 2021 International Conference on Artificial Intelligence (ICAI), 5-7 April 2021, Islamabad, Pakistan.

Full content URL: https://doi.org/10.1109/ICAI52203.2021.9445253

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Performance Evaluation of Background Subtraction Techniques for Video Frames
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Item Type:Conference or Workshop contribution (Paper)
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Abstract

The fundamental working of background subtraction is to identify the moving region by taking pixel-wise difference of the current frame from the previous one. The proposed study presents the comparison and implementation of different background subtraction techniques i.e., frame-difference method, mixture of Gaussian model 2 (MOG2) and k-nearest neighbor (KNN) for background subtraction. For all the three techniques, prior to segmentation, background modeling and then features extraction steps are performed. It is investigated that on the same dataset, frame-difference technique outperforms both MOG2 and KNN and achieve accuracy of 89.98%, recall of 94.43% precision 79.55% and F1-score of 81.42%.

Keywords:Computer vision, background subtraction
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:46505
Deposited On:25 Oct 2021 08:08

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