Fast Compressive Target Detection for Wireless Video Sensor Nodes
- 204 Downloads
In order to prolong the lifetime of visual target detection and tracking system based on wireless video sensor networks, many efficient methods have been proposed to reduce the energy consumption of the battery-powered video sensor nodes. Focused on reducing the amount of image data for computing, this paper presents a fast compressive method of target detection for video sensor nodes using structured compressive sensing. The major contributions are as follows: Firstly, we construct a novel structured measurement matrix for sampling the image. Secondly, we use an efficient adaptive Gaussian mixture model for real-time background subtraction. Experimental results show that our method can achieve good performance and over two times faster than traditional Gaussian mixture model.
KeywordsTarget detection Video sensor nodes Compressive sensing Structured measurement matrix
This work was supported by the National Natural Science Foundation of China under Grant 61271274, Natural Science Foundation of Hubei province, China, under Grants 2012FFA108 and 2013BHE009. Wuhan Youth Chenguang Program of Science and Technology (2014070404010209).
Compliance with Ethical Standards
Conflict of interest
The authors declare that there is no conflict of interests regarding the publication of this article.
- 2.Qi Dai, Wei Sha. “The physics of compressive sensing and the Gradient-based recovery algorithms”. 2009, ArXiv: 0906.1487.Google Scholar
- 4.Garrett Warnell, Dikpal Reddy. “Adaptive rate compressive sensing for background substraction”, ICASSP: 1477–1480, 2012Google Scholar
- 5.Y. Benezeth, P. Jodoin, B. Emile, H. Laurent, and C. Rosenberger, “Review and evaluation of commonly-implemented background subtraction algorithms,” in Proc. IEEE Int. Conf. Pattern Recognition. Dec 2008, pp. 1–4.Google Scholar
- 6.Yiran Shen, Wen Hu, “Efficient Background Subtraction for Real-time Tracking in Embedded Camera Networks”, 2012.Google Scholar
- 8.Holger Rauhut, “Circulant and Toeplitz matrices in compressed sensing”, In Processing SPARS09, Saint Malo, 2009Google Scholar
- 9.Waheed Bajwa, Jarvis Haupt. “Compressive Wireless Sensing”, IPSN ‘06 Proceedings of the 5th international conference on Information processing in sensor networks, 2012, 134–142.Google Scholar
- 10.Zai Yang, Cishen Zhang, and Lihua Xie, “Robustly stable signal recovery in compressed sensing with structured matrix perturbation”. IEEE Trans. Signal Processing, 2012.Google Scholar
- 12.O. Barnich and M. Van Droogenbroeck, “ViBe: A powerful random technique to estimate the background in video sequences,” in Proc. Int. Conf. Speech Signal Process, Apr. 2009, pp. 945–948.Google Scholar