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Compressed Sensing based Foreground Detection Vector for Object Detection in Wireless Visual Sensor Networks

Publication Type : Journal Article

Publisher : Elsevier’sAEUE International Journal of Electronics and Communications

Source : Elsevier’sAEUE International Journal of Electronics and Communications, 2017

Url : https://www.researchgate.net/publication/311917957_Compressed_Sensing_based_Foreground_Detection_Vector_for_Object_Detection_in_Wireless_Visual_Sensor_Networks

Campus : Chennai

School : School of Engineering

Department : Computer Science

Year : 2017

Abstract : 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.

Cite this Research Publication : Dr. S. Aasha Nandhini, Radha Sankararajan, Manimozhi Swaminathan, "Compressed Sensing based Foreground Detection Vector for Object Detection in Wireless Visual Sensor Networks” Elsevier’sAEUE International Journal of Electronics and Communications, 2017
DOI:10.1016/j.aeue.2016.12.020

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