关键词: 车辆检测, 形状聚类, 目标候选区域, 级联分类器

Abstract: An efficient car detection method is proposed to detect cars with arbitrary aspect ratios or angles, which is based on vehicle shape features, object proposals and cascade Boosting classifiers. The train dataset is clustered according to different angles or aspect ratios of cars firstly. Then the Accumulated Channel Features（ACF） of car areas are extracted, which is learned by AdaBoost to detect cars. Moreover, the object proposal, which is calculated by EdgeBox, is used instead of sliding windows for computation issues. A dataset with three difficulty levels, which consists of more than 3?500 images, is used to test the proposed method. The experimental results indicate that compared with the ACF-based, the DPM-based, the NPD-based and the HOG-Haar-based methods, the proposed method is the best with an average detection accuracy of 85% on all test sets with different difficulty levels.

Key words: vehicle detection, shape clustering, object proposals, cascade classifier