Compressed sensing based foreground detection vector for object detection in Wireless Visual Sensor Networks

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• 作者：Aasha Nandhini Sukumaran ; ^{aashanandhinis@ssn.edu.in} ; Radha Sankararajan¹ ; ^{radhas@ssn.edu.in} ; Manimozhi Swaminathan ^{manimozhi1313cs@ece.ssn.edu.in}
• 关键词：Compressive sensing ; Background subtraction ; Foreground detection vector ; Transmission energy ; WVSN
• 刊名：AEU - International Journal of Electronics and Communications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：72
• 期：Complete
• 页码：216-224
• 全文大小：1499 K
• 卷排序：72

文摘

Compressed sensing based background subtraction (CS-BS) plays a significant role in video surveillance applications in Wireless Visual Sensor Networks. This paper implements a CS-BS framework with a novel thresholding strategy to detect the anomaly with fewer measurements in a secured indoor environment. In CS-BS, the CS is performed on the difference frame which is sparse, thereby reducing energy, memory and bandwidth. In this framework, a foreground threshold is proposed based on the measurement matrix to extract the moving object from a scene. The performance of the CS-BS framework with FDV is evaluated using metrics such as detection accuracy, energy complexity, percentage of reduction in samples and measurements. The proposed CS-BS framework with hybrid matrix based FDV achieves around 95.8% reduction of measurements and 91% reduction of samples.