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
A monocular solution to vision-based ACC in road vehicles
EUROCAST'05 Proceedings of the 10th international conference on Computer Aided Systems Theory
A visual blindspot monitoring system for safe lane changes
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
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This paper describes a target detection system on road environments based on Support Vector Machine (SVM) and monocular vision. The final goal is to provide 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 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 images has been created for learning purposes. The classifier is trained using SVM in order to be able to classify cars and 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.