Vehicle Detection Based on Deep Dual-Vehicle Deformable Part Models

Abstract

Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology.