19 December 2017 Enhanced compressed sensing for visual target tracking in wireless visual sensor networks
Qiang Guo
Author Affiliations +
Funded by: National Natural Science Foundation of China (NSFC), Natural Science Foundation of China, General Program of Education Bureau of Liaoning Province of China, Science Research Foundation for the General Program of Education Bureau of Liaoning Province of China
Abstract
Moving object tracking in wireless sensor networks (WSNs) has been widely applied in various fields. Designing low-power WSNs for the limited resources of the sensor, such as energy limitation, energy restriction, and bandwidth constraints, is of high priority. However, most existing works focus on only single conflicting optimization criteria. An efficient compressive sensing technique based on a customized memory gradient pursuit algorithm with early termination in WSNs is presented, which strikes compelling trade-offs among energy dissipation for wireless transmission, certain types of bandwidth, and minimum storage. Then, the proposed approach adopts an unscented particle filter to predict the location of the target. The experimental results with a theoretical analysis demonstrate the substantially superior effectiveness of the proposed model and framework in regard to the energy and speed under the resource limitation of a visual sensor node.
© 2017 SPIE and IS&T 1017-9909/2017/$25.00 © 2017 SPIE and IS&T
Qiang Guo "Enhanced compressed sensing for visual target tracking in wireless visual sensor networks," Journal of Electronic Imaging 26(6), 063028 (19 December 2017). https://doi.org/10.1117/1.JEI.26.6.063028
Received: 9 August 2017; Accepted: 28 November 2017; Published: 19 December 2017
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KEYWORDS
Visualization

Optical tracking

Sensor networks

Sensors

Visual compression

Compressed sensing

Video

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