Vehicle ROI Extraction Based on Area Estimation Gaussian Mixture Model

The extraction of vehicle region of interest (ROI) is a key step of vehicle detection system. When Gaussian Mixture Model (GMM) is applied to vehicle ROI extraction, the extraction speed is unsatisfied and the results remain some noise blocks. In order to solve these problems, a method of vehicle ROI extraction based on area estimation Gaussian Mixture Model is proposed. First, Gaussian mixture background model based on scale mapping is used for background detection and foreground clump of samples extraction. Then, the vehicle area estimation model is trained using the automatic samples collection and selection mechanism. After that, the model is used for foreground clumps selection, which can finally get the vehicle ROI. Experiments show that this method can provide more accurate vehicle ROI for the follow-up of vehicle detection, thus improves the real-time and reliability performance of vehicle detection.

[1]  Jonathan R. Williford,et al.  Spatial multimodal mean background model for real-time MTI , 2009, Defense + Commercial Sensing.

[2]  Osama Masoud,et al.  Detection and classification of vehicles , 2002, IEEE Trans. Intell. Transp. Syst..

[3]  Intan Sari Areni,et al.  Vehicle detection and tracking using Gaussian Mixture Model and Kalman Filter , 2016, 2016 International Conference on Computational Intelligence and Cybernetics.

[4]  Zezhi Chen,et al.  Vehicle detection, tracking and classification in urban traffic , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[5]  Atsushi Shimada,et al.  Background Modeling Based on Bidirectional Analysis , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Masayoshi Tomizuka,et al.  Automated highway systems - an intelligent transportation system for the next century , 1997, Proceedings of IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[7]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Ashish Ghosh,et al.  Moving object detection using Gaussian background model and Wronskian framework , 2013, 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[9]  Murat Kunt,et al.  Spatiotemporal Segmentation Based on Region Merging , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  L. Wixson.  Detecting Salient Motion by Accumulating Directionally-Consistent Flow , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Chun-Jen Chen,et al.  A linear-time component-labeling algorithm using contour tracing technique , 2004, Comput. Vis. Image Underst..

[12]  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).

[13]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[14]  R. Gunasekaran,et al.  Region-based Saliency Detection and Its Application in Object Recognition , 2015 .

[15]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[16]  Chong-Min Kyung,et al.  Optimized learning rate for energy waste minimization in a background subtraction based surveillance system , 2013, 2013 IEEE International Symposium on Circuits and Systems (ISCAS2013).