Real-time traffic anomaly detection based on Gaussian mixture model and hidden Markov model

With the rapid development and wide application of visual sensor technology and image processing technology, the traffic anomaly events detection methods of intelligent transportation system (ITS) are being constantly updated. Traditional machine learning methods do not require high computing power but lack high accuracy, while deep learning methods provide high accuracy but demand of high computing power for edge computing hardware. To improve accuracy and real-time performance of detecting abnormal traffic events in edge computing hardware, a novel traffic anomaly detection model was developed in the following steps. In the first moving object tracking stage, a hybrid model was designed by combining Gaussian mixture model (GMM) with hidden Markov model, and optimized the tracking accuracy of target trajectories in multiframe images from traffic surveillance video. In the second abnormal events classification stage, principal component analysis was applied to reduce the dimension of the trajectories features, and then the abnormal traffic behaviors were classified by K-Nearest Neighbor. Experiments demonstrate that the model can achieve higher detection accuracy than GMM method as well as a faster detection speed than You only look once deep learning method in moving object tracking stage.