Parallel Priority Region Approach to Detect Background
Background detection is essential in video analyses; optimization is often needed in order to achieve real time calculation. Information gathered by dual cameras placed in the front and rear part of an Autonomous Vehicle (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 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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088490Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1262
 C. Stauffer and W. Grimson, "Adaptive background mixture models for real-time tracking", in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, 1999, pp. 246–252.
 O. Javed, K. Shafique and M. Shah, "A hierarchical approach to robust background subtraction using color and gradient information", in: Proceedings Workshop.
 A. M. Elgammal, D. Harwood and L. S. Davis, "Non-parametric model for back- ground subtraction", in: Proceedings of the 6th European Conference on Computer Vision-Part II, 2000, pp. 751–767.
 A. Elgammal, David H. and L. Davis "Non-parametric Model for Background Subtraction", Computer Vision Laboratory, University of Maryland, College Park, MD 20742, USA, 2000.
 E. Monteiro, B. Vizzotto and C. Diniz "Parallelization of Full Search Motion Estimation Algorithm for Parallel and Distributed Platforms".
 D. Park and H. Byun "A unified approach to background adaptation and initialization in public scenes", Department of Computer Science, Engineering.
 J. M. Geusebroek, A. W. M. Smeulders, and J. van de Weijer, “Fast anisotropic gauss filtering,” IEEE Transactions on Image Processing, vol. 12, no. 8, pp. 938–943, 2003.
 Y. Zhang, Chenyao Geng, Danya Yao, Lihui Peng, "Real-time Traffic Object Detection Technique Based on Improved Background Differencing Algorithm".
 M. McNaughton, "Parallel Algorithms for Real-time Motion Planning" 2011, University Autonomous Driving Collaborative Research Laboratory, CMU-RI-TR-xx-xx.