Overtaking vehicle detection using a spatio-temporal CRF

Overtaking vehicle detection is vital for road safety, as the dangerous behavior of that vehicle may affect the safety of ego-vehicle and the time is not enough for the driver to attend and react. Therefore, it is one of the key components of the Advanced Driver Assistance Systems. Mostly, traditional methods only use local information, appearance or motion. In this paper, we build a novel CRF model to make use of the interaction between local regions, and the motion features from multiple scales as well. The whole model is based on the low-level optical flows. In order to increase the robustness to the noise in the flow, we divided the motion field into small blocks, and learned Mixture of Probabilistic Principle Analysis models for the common motion patterns of the background. Moreover, we also adopted an online scheme for updating the parameters. Results of testing on the real road images demonstrated the capability of the proposed algorithm.