19 December 2017 Enhanced compressed sensing for visual target tracking in wireless visual sensor networks
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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
Qiang Guo, 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 . Submission: Received: 9 August 2017; Accepted: 28 November 2017
Received: 9 August 2017; Accepted: 28 November 2017; Published: 19 December 2017
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