Modifying open source tools for video pre-processing to achieve ultra low bandwidth for video surveillance over delay tolerant networks

We propose a solution for efficiently transforming video data over a delay tolerant network. We extract and transport only relevant features from the video frame at a minimal computational cost using low cost COTS embedded environment.

[1]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[2]  Anamitra Makur,et al.  Object-based Surveillance Video Compression using Foreground Motion Compensation , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[3]  Jean-Marc Odobez,et al.  Multi-Layer Background Subtraction Based on Color and Texture , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Lingyun Yu,et al.  Extraction of Human Body Skeleton Based on Silhouette Images , 2010, 2010 Second International Workshop on Education Technology and Computer Science.

[5]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Tsung-Han Tsai,et al.  Exploring Contextual Redundancy in Improving Object-Based Video Coding for Video Sensor Networks Surveillance , 2012, IEEE Transactions on Multimedia.

[7]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[8]  Jian Yao,et al.  Fast human detection from videos using covariance features , 2008, ECCV 2008.

[9]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[10]  Santanu Chaudhury,et al.  Parametric video compression using appearance space , 2008, 2008 19th International Conference on Pattern Recognition.

[11]  Hironobu Fujiyoshi,et al.  Real-time human motion analysis by image skeletonization , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[12]  Khalid Saeed,et al.  K3M: A universal algorithm for image skeletonization and a review of thinning techniques , 2010, Int. J. Appl. Math. Comput. Sci..

[13]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.