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A Vision based Vehicle Detection System

Himanshu Chandel, Sonia Vatta. Published in Pattern Recognition.

Communications on Applied Electronics
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Himanshu Chandel, Sonia Vatta

Himanshu Chandel and Sonia Vatta. Article: A Vision based Vehicle Detection System. Communications on Applied Electronics 2(6):6-16, August 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Himanshu Chandel and Sonia Vatta},
	title = {Article: A Vision based Vehicle Detection System},
	journal = {Communications on Applied Electronics},
	year = {2015},
	volume = {2},
	number = {6},
	pages = {6-16},
	month = {August},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


In recent years, automotive manufacturers have equipped their vehicles with innovative Advanced Driver Assistance Systems (ADAS) to ease driving and avoid dangerous situations, such as unintended lane departures or collisions with other road users, like vehicles and pedestrians. To this end, ADAS at the cutting edge are equipped with cameras to sense the vehicle surrounding. This research work investigates the techniques for monocular vision based vehicle detection. A system that can robustly detect and track vehicles in images. The system consists of three major modules: shape analysis based on Histogram of oriented gradient (HOG) is used as the main feature descriptor, a machine learning part based on support vector machine (SVM) for vehicle verification, lastly a technique is applied for texture analysis by applying the concept of gray level co-occurrence matrix (GLCM). More specifically, we are interested in detection of cars from different camera viewpoints, diverse lightning conditions majorly images in sunlight, night, rain, normal day light, low light and further handling the occlusion. The images has been pre-processed at the first step to get the optimum results in all the conditions. Experiments have been conducted on large numbers of car images with different angles. For car images the classifier contains 4 classes of images with the combination of positive and negative images, the test and train segments. Due to length of long feature vector we have deduced it using different cell sizes for more accuracy and efficiency. Results will be presented and future work will be discussed.


  1. Adnan Shaout, Dominic Colella, S. Awad, “Advanced Driver Assistance Systems
  2. Past, Present and Future,” Seventh International Computer Engineering Conference (ICENCO), 2011, pp. 321–329.
  3. Andreas Geiger, Philip Lenz and Raquel Urtasun, “Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3354 - 3361.
  4. Advance driver assistance systems
  5. LG - Safety & Convenience Devices, Advance driver assistance systems
  6. based advance driver assistance
  7. Whei Zhang,Q.M Jonathan Wu,Xiaokang Yang, “Multilevel framework to detect and handle Occlusion,” IEEE Transaction on Intelligent Transport Systems,VOL.9,NO.1,2008
  8. Himanshu Chandel and Sonia Vatta, “Occlusion Detection and Handling: A Review,” International Journal of Computer Applications (0975 – 8887) Volume 120 – No.10, June 2015.
  9. SLAM for dummies
  10. Herbert Bay, Tinne Tuytelaars, Luc Van Gool ,“SURF: Speeded Up robust features ,”15th International Conference on Pattern Recognition,” 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I , pp. 404-417
  11. P. Felzenszwalb, D. McAllester, D. Ramanan,“ A discriminatively trained, multiscale, deformable part model,” IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008. pp. 1 – 8.
  12. Navneet Dalal and Bill Triggs, “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 886 - 893 vol. 1.
  13. Ro´merRosales and StanSclaroff, “Improved Tracking of Multiple Humans with Trajectory Prediction and Occlusion Modeling,” IEEE Conf. on Computer Vision and Pattern Recognition,1998
  14. Davi Geiger,Bruce,Ladendorf,Alan Yuille, “Occlusions and Binocular Stereo,”International Journal of Computer Vision,1995
  15. Nello Cristianini and John Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-based Learning Methods”, Cambridge University Press, 2000.
  16. James J. Little and Walter E. Gillet, “Direct Evidence for Occlusion in Stereo and Motion,” Computer Vision — ECCV, 1990
  17. Sing Bing Kang, Szeliski R. , Jinxiang Chai, “Handling occlusions in dense multi-view stereo,” Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. I-103 - I-110 vol.1.


Vision, texture, vehicle, car, autonomous, shape