Vehicle and Pedestrian Detection Applications

This paper describes a target detection system on road environments based on Support Vec-tor Machine (SVM) and monocular vision. The fi-nal goal is to provide pedestrian-to-car and car-to-car time gap. The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic fea-ture of the detected objects are first located in the image using vision and then combined with a SVM- based classifier. An intelligent learning approach is proposed in order to better deal with objects vari-ability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples has been created for learning pur-poses. The classifier is trained using SVM in order to be able to classify pedestrians, cars and trucks. In the paper, we present and discuss the results achieved up to date in real traffic conditions.

[1]  Larry S. Davis,et al.  Probabilistic template based pedestrian detection in infrared videos , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[2]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[3]  Amnon Shashua,et al.  Vision-based ACC with a single camera: bounds on range and range rate accuracy , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[4]  Glenn R Widmann,et al.  DEVELOPMENT OF COLLISION AVOIDANCE SYSTEMS AT DELPHI AUTOMOTIVE SYSTEMS , 1998 .

[5]  C. Hilario,et al.  Visual Perception and Tracking of Vehicles for Driver Assistance Systems , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[6]  Dominique Gruyer,et al.  Cooperative Fusion for Multi-Obstacles Detection With Use of Stereovision and Laser Scanner , 2005, Auton. Robots.

[7]  A. Shashua,et al.  Pedestrian detection for driving assistance systems: single-frame classification and system level performance , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[8]  D. Fernandez,et al.  Night time vehicle detection for driving assistance lightbeam controller , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[9]  Miguel Ángel Sotelo,et al.  A Monocular Solution to Vision-Based ACC in Road Vehicles , 2005, EUROCAST.

[10]  Dariu Gavrila,et al.  The Issues , 2011 .

[11]  Massimo Bertozzi,et al.  Shape-based pedestrian detection , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[12]  Nanning Zheng,et al.  Pedestrian detection using sparse Gabor filter and support vector machine , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

[13]  Ignacio Parra,et al.  Combination of Feature Extraction Methods for SVM Pedestrian Detection , 2007, IEEE Transactions on Intelligent Transportation Systems.

[14]  P. C. Antonello,et al.  Multi-resolution vehicle detection using artificial vision , 2004, IEEE Intelligent Vehicles Symposium, 2004.