Background detection is essential in video analyses; optimization is often needed in order to achieve real time calculation. Information gathered by *dual cameras<\/em> placed in the front and rear part of an Autonomous Vehicle<\/em> (AV) is integrated for background detection. In this paper, real time calculation is achieved on the proposed technique by using Priority Regions (PR) and Parallel Processing<\/em> together where each frame is divided into regions then and each region process is processed in parallel. PR division depends upon driver view limitations. A background detection system is built on the Temporal Difference (TD) and Gaussian Filtering (GF). Temporal Difference and Gaussian Filtering with multi threshold and sigma (weight) value are be based on PR characteristics. The experiment result is prepared on real scene. Comparison of the speed and accuracy with traditional background detection techniques, the effectiveness of PR and parallel processing are also discussed in this paper.<\/p>\r\n","references":"[1] C. Stauffer and W. Grimson, \"Adaptive background mixture models for \r\nreal-time tracking\", in: IEEE Computer Society Conference on \r\nComputer Vision and Pattern Recognition, vol. 2, 1999, pp. 246\u2013252. \r\n[2] O. Javed, K. Shafique and M. Shah, \"A hierarchical approach to robust \r\nbackground subtraction using color and gradient information\", in: \r\nProceedings Workshop. \r\n[3] A. M. Elgammal, D. Harwood and L. S. Davis, \"Non-parametric model \r\nfor back- ground subtraction\", in: Proceedings of the 6th European \r\nConference on Computer Vision-Part II, 2000, pp. 751\u2013767. \r\n[4] A. Elgammal, David H. and L. Davis \"Non-parametric Model for \r\nBackground Subtraction\", Computer Vision Laboratory, University of \r\nMaryland, College Park, MD 20742, USA, 2000. \r\n[5] E. Monteiro, B. Vizzotto and C. Diniz \"Parallelization of Full Search \r\nMotion Estimation Algorithm for Parallel and Distributed Platforms\". \r\n[6] D. Park and H. Byun \"A unified approach to background adaptation and \r\ninitialization in public scenes\", Department of Computer Science, \r\nEngineering. \r\n[7] J. M. Geusebroek, A. W. M. Smeulders, and J. van de Weijer, \u201cFast \r\nanisotropic gauss filtering,\u201d IEEE Transactions on Image Processing, \r\nvol. 12, no. 8, pp. 938\u2013943, 2003. \r\n[8] Y. Zhang, Chenyao Geng, Danya Yao, Lihui Peng, \"Real-time Traffic \r\nObject Detection Technique Based on Improved Background \r\nDifferencing Algorithm\". \r\n[9] M. McNaughton, \"Parallel Algorithms for Real-time Motion Planning\" \r\n2011, University Autonomous Driving Collaborative Research \r\nLaboratory, CMU-RI-TR-xx-xx. ","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 82, 2013"}*