Performance evaluation of background subtraction algorithms for Android devices deployed in Wireless Multimedia Sensor Networks

With the increased use of smart phones, Wireless Multimedia Sensor Networks (WMSNs) will have opportunities to deploy such devices in several contexts for data collection and processing. While smart phones come with richer resources and can do complex processing, their battery is still limited. Therefore, data reduction techniques can be used on these devices to reduce energy consumption. One of the common techniques for energy reduction is background subtraction, which has been used for camera sensors in WMSNs. In this paper, we investigate the performance of various BS algorithms on Android devices in terms of computation and communication energy, time and quality. To this end, we picked five different BS algorithms and implemented them in an Android platform. Considering the fact that these BS algorithms will be run within the context of WMSNs where the data is subject to packet losses and errors, we also investigated the performance in terms of packet loss ratio in the network under various packet sizes. The experiment results indicated that the most energy-efficient BS algorithm could also provide the best quality in terms of the foreground detected. The results also indicate that BS algorithms can provide significant energy savings in terms of transmission energy costs.

[1]  Martin Reisslein,et al.  Towards Efficient Wireless Video Sensor Networks: A Survey of Existing Node Architectures and Proposal for A Flexi-WVSNP Design , 2011, IEEE Communications Surveys & Tutorials.

[2]  Ferdinand van der Heijden,et al.  Efficient adaptive density estimation per image pixel for the task of background subtraction , 2006, Pattern Recognit. Lett..

[3]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[5]  Sidney S. Fels,et al.  Evaluation of Background Subtraction Algorithms with Post-Processing , 2008, 2008 IEEE Fifth International Conference on Advanced Video and Signal Based Surveillance.

[6]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[7]  Laure Tougne,et al.  Background Models Challenge, Workshop of ACCV 2012 , 2012, ACCV 2012.

[8]  Kemal Akkaya,et al.  Detecting and connecting disjoint sub-networks in wireless sensor and actor networks , 2009, Ad Hoc Networks.

[9]  Hélène Laurent,et al.  Comparative study of background subtraction algorithms , 2010, J. Electronic Imaging.

[10]  Ahmed Helmy,et al.  Comparing Background Subtraction Algorithms and Method of Car Counting , 2012, ArXiv.

[11]  Laure Tougne,et al.  A testing framework for background subtraction algorithms comparison in intrusion detection context , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

[12]  Pinar Sarisaray Boluk.  Performance comparisons of the image quality evaluation techniques in Wireless Multimedia Sensor Networks , 2013, Wirel. Networks.

[13]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[14]  Pascal Fua,et al.  Making Background Subtraction Robust to Sudden Illumination Changes , 2008, ECCV.

[15]  Senior Member,et al.  Robust Background Subtraction for Network Surveillance in H . 264 Streaming Video , 2013 .

[16]  José María Martínez Sanchez,et al.  Comparative Evaluation of Stationary Foreground Object Detection Algorithms Based on Background Subtraction Techniques , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[17]  Ian F. Akyildiz,et al.  Wireless multimedia sensor networks: A survey , 2007, IEEE Wireless Communications.

[18]  Alan M. McIvor,et al.  Background Subtraction Techniques , 2000 .

[19]  Jesús Bescós,et al.  Background Subtraction Techniques: Systematic Evaluation and Comparative Analysis , 2009, ACIVS.

[20]  Kemal Akkaya,et al.  Performance evaluation of wireless mesh networks using IEEE 802.11s and IEEE 802.11n , 2012, 2012 IEEE International Conference on Communications (ICC).

[21]  Najeem Lawal,et al.  Low Complexity Background Subtraction for Wireless Vision Sensor Node , 2013, 2013 Euromicro Conference on Digital System Design.

[22]  M. Tech,et al.  Background Subtraction Techniques: Systematic Evaluation and Comparative Analysis , 2013 .

[23]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Nigel J. B. McFarlane,et al.  Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.

[25]  Benjamin Höferlin,et al.  Evaluation of background subtraction techniques for video surveillance , 2011, CVPR 2011.

[26]  Alex Zelinsky,et al.  Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf] , 2009, IEEE Robotics & Automation Magazine.