Forward vehicle detection method based on geometric constraint and multi-feature fusion

Vehicle detection is still a challenging task for intelligent vehicle platform. Real-time requirements and vehicle posture changes, illumination conditions, occlusion levels are the main difficulties. To handle these difficulties, a new algorithm for vehicle detection is proposed. A region of interest for an image is obtained by using the improved geometric constraints algorithm, and then the integral images are used to accelerate the feature extraction process within the region of interest. Finally, Multi-feature fusion algorithm is performed based on the confidence scores of the Gentle Adaboost classifications that are trained by Haar-like feature, HOG feature and LBP feature respectively. In the testing phase, the three confidence scores of the classifier are used to determine the classification results. The experimental results show that the proposed method can reduce the detection time effectively and improve the accuracy of vehicle detection.