Single Camera Vehicle Detection Using Edges and Bag-of-Features

Vehicle detection is becoming a necessary part of Automatic Cruise Control (ACC) and Advanced Driver Assistance Systems (ADAS). Our main focus in this paper is on improving the performance of single camera based vehicle detection systems. Edges are one of the main characteristics of an object, which carries most of the information about an object in an image. In this paper, it was observed that horizontal edges are strong feature for vehicle detection. Therefore, we generated initial candidate using Horizontal Edge Filtering (HEF) on canny edge map. These initial candidates are further verified using Bag-of-Features (BoF) with K nearest neighbor algorithm. A threshold is used on differences of histograms of training and test images for matching the vehicles. In this paper, the combination of edges (initial candidate) and bag-of-features (final verification) has improved detection rate significantly as compared to other well known methods. Our method has 96% detection rate on roads inside a city and 98% detection on highways.

[1]  Zuwena Musoromy,et al.  Edge detection comparison for license plate detection , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[2]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Zehang Sun,et al.  On-road vehicle detection using evolutionary Gabor filter optimization , 2005, IEEE Transactions on Intelligent Transportation Systems.

[4]  Tarak Gandhi,et al.  Vehicle Surround Capture: Survey of Techniques and a Novel Omni-Video-Based Approach for Dynamic Panoramic Surround Maps , 2006, IEEE Transactions on Intelligent Transportation Systems.

[5]  Chris J. Harris,et al.  VEHICLE DETECTION AND RECOGNITION IN GREYSCALE IMAGERY , 1995 .

[6]  Mohan M. Trivedi,et al.  Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation , 2006, IEEE Transactions on Intelligent Transportation Systems.

[7]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[8]  Thomas Kalinke,et al.  An image processing system for driver assistance , 2000, Image Vis. Comput..

[9]  Tarak Gandhi,et al.  Looking-In and Looking-Out of a Vehicle: Computer-Vision-Based Enhanced Vehicle Safety , 2007, IEEE Transactions on Intelligent Transportation Systems.

[10]  G. Bebis,et al.  On-road vehicle detection using optical sensors: a review , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[11]  Zehang Sun,et al.  On-road vehicle detection: a review , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Tarak Gandhi,et al.  Computer Vision and Machine Learning for Enhancing Pedestrian Safety , 2008, Computational Intelligence in Automotive Applications.

[13]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[14]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[15]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[16]  Yuan-yuan Ren,et al.  Study on the Edge Detection Algorithms of Road Image , 2010, 2010 Third International Symposium on Information Processing.

[17]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.