Detection of Front-View Vehicle with Occlusions Using AdaBoost

In this paper, we propose a vehicle detection method based on AdaBoost. We focus on the detection of front-view car and bus with occlusions on highway. Samples with different occlusion situations are selected into the training set. By using basic and rotated Haar-like features extracted from the samples in the set, we train an AdaBoost-based cascade vehicle detector. The performance tests on static images and short time videos show that (1) our approach detects cars more effectively than buses (2) the real-time detection of our method on video proceeds at 30 frames per second.