Abnormal driving behavior detection based on kernelization-sparse representation in video surveillance

Abstract

The detection of abnormal driving behaviors based on video surveillance systems is an important part of Intelligent Transportation System (ITS), which can help reduce disturbances on traffic flow and improve traffic safety. First, the study proposes a novel nonlinear sparse reconstruction method for abnormal driving behavior detection in video surveillance. A hybrid kernel function formed by convexly combining a local kernel of radial basis function (RBF) and a global kernel of homogeneous polynomial is been applied in sparse reconstruction method. Then, a novel Hybrid Kernel Orthogonal Matching Pursuit (HKOMP) algorithm is designed to solve the proposed sparse reconstruction model. Finally, the performance of the abnormal detection method is tested on two datasets i.e. stop sign dataset and car parking dataset. In addition, comparative experiments with five classical methods are carried out. The experimental results indicate that the proposed method outperforms other five comparison methods in terms of accuracy.