Vehicle detection based on the deformable hybrid image template

In complex urban traffic conditions, the accurate detection of vehicles is challenging to current vehicle detection methods. To achieve the precise vehicle detection in complex urban traffic conditions, we have proposed a vehicle detection method based on a deformable hybrid image template in this paper. Our method contains two steps: constructing our hybrid image template and its probability model, and detecting vehicles from traffic images by using a three-stage SUM-MAX procedure. In the template construction step, small image patches constituting the hybrid image template are automatically learned from training images. These image patches have various features and the feature types include sketch, texture, flatness, and color. Furthermore, a probability model is proposed to assist the vehicle detection step. After the template construction, a three-stage SUM-MAX procedure is applied to achieve vehicle detection with local deformations in locations and orientations. There are two innovations in our method which are the applications of the hybrid image template and the three-stage SUM-MAX procedure in vehicle detection under complex urban traffic conditions. To evaluate our method, we have done a quantitative and contrast experiment and the experiment on complex urban traffic conditions. The experimental results show that our method can effectively cope with various vehicle poses, vehicle shapes, time-of-day and weather conditions. In particular, our method has good performance in complex urban traffic conditions.