A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A monocular solution to vision-based ACC in road vehicles
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
Combination of Feature Extraction Methods for SVM Pedestrian Detection
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Image Processing
Automatic vehicle identification for Argentinean license plates using intelligent template matching
Pattern Recognition Letters
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This paper describes a vehicle detection system based on support vector machine (SVM) and monocular vision. The final goal is to provide vehicle-to-vehicle time gap for automatic cruise control (ACC) applications in the framework of intelligent transportation systems (ITS). 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 feature 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 variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road scenes has been created for learning purposes. The classifier is trained using SVM in order to be able to classify vehicles, including trucks. In addition, the vehicle detection system described in this paper provides early detection of passing cars and assigns lane to target vehicles. In the paper, we present and discuss the results achieved up to date in real traffic conditions.