A hidden Markov model framework for traffic event detection using video features

A novel approach for highway traffic event detection in video is presented. The proposed algorithm extracts event features directly from compressed video and detects traffic event using a Gaussian mixture hidden Markov model (GMHMM). First, an invariant feature vector is extracted from discrete cosine transform (DCT) domain and macro-block vectors after MPEG video stream is parsed. The extracted feature vector accurately describes the change of traffic state and is robust towards different camera setups and illumination situations, such as sunny, cloud, and night. Six traffic patterns are studied and a GMHMM is trained to model these patterns in offline stage. Then, Viterbi algorithm is used to determine the most likely traffic condition. The proposed algorithm is efficient both in terms of computational complexity and memory requirement. The experimental results prove the system has a high detection rate. The presented model based system can be easily extended for detection of similar traffic events.