关键词: 智能交通, 车辆检测, 微型神经网, LNPD特征, 级联分类器

Abstract: The vehicle detection is a critical step for intelligent traffic system construction, while the detection method using single view cannot accurately detect the vehicle specific features in the complex traffic scene such as illumination change and shelter. To improve the accuracy of vehicle detection in traffic surveillance images, embedding the AdaBoost algorithm in the micro neural network and combining Local Normalized Pixel-value Difference（LNPD） features, this paper proposes a multi-view vehicle detection method based on cascade micro neural network. The method first extracts local normalized pixel-value difference features for images and then learns optimal feature subset and its combination features using multilayer perceptron. Finally, the AdaBoost algorithm is employed to filter discriminative features and construct a strong classifier. In the three real traffic scenarios with different complexity, a dataset with front, side and back three viewpoints is used as a test set, and the test results are compared with NPD, DPM-V5, ACF and RCNN adopting qualitative and quantitative analyses. The experimental results show that the proposed method is optimal with an average detection accuracy of 82.28% and an elapsed time of 125 ms.

Key words: intelligent transportation, vehicle detection, micro neural networks, LNPD feature, cascade classifier