EVBS-CAT: enhanced video background subtraction with a controlled adaptive threshold for constrained wireless video surveillance

被引:0
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作者
Ahcen Aliouat
Nasreddine Kouadria
Moufida Maimour
Saliha Harize
机构
[1] Badji Mokhtar-Annaba University,Laboratory of Automatic and Signals of Annaba (LASA), Electronics Department, Faculty of Technology
[2] IMT Atlantique,undefined
[3] Lab-STICC,undefined
[4] UMR CNRS 6285,undefined
[5] Université de Lorraine,undefined
[6] CNRS,undefined
[7] CRAN,undefined
来源
Journal of Real-Time Image Processing | 2024年 / 21卷
关键词
Object detection; Background subtraction; WMSN; Object-based video coding;
D O I
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中图分类号
学科分类号
摘要
Moving object detection (MOD) has gained significant attention for its application in advanced video surveillance tasks. Region-of-Interest (ROI) detection algorithms are essential prerequisites for various applications, ranging from video surveillance to adaptive video coding. The simplicity and efficiency of MOD methods are critical when targeting energy-constrained systems, such as Wireless Multimedia Sensor Networks (WMSN). The challenge is always to reduce computational costs while preserving high detection accuracy. In this article, we present EVBS-CAT, an Enhanced Video Background Subtraction with a Controlled Adaptive Threshold selection method for low-cost surveillance systems. The proposed moving object detection method utilizes background subtraction (BS) with morphological operations and adaptive thresholding. We evaluate the algorithm using the Change Detection 2012 dataset. Through a computational complexity analysis of each step, we demonstrate the efficiency of the proposed MOD technique for embedded WMSN. The algorithm yields promising results compared to state-of-the-art MOD techniques in the context of embedded wireless surveillance.
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