road traffic model using distributed camera network
Traffic monitoring/prediction using a distributed camera network is presented in this paper. The activities on each road link are monitored and features are derived to identify the pattern. Then it is learnt, classified, predicted and communicated to neighboring road links. We used GMM-EM based classification and HMM based prediction. Optimum path is determined by assigning proportional weights to the predicted states of the connected road links. The proposed method is neither based on tracking nor on vehicle detection. Apart from this the method is flexible, adaptive, robust and computationally light. Unlike the existing methods it does not assume or draws analogies of traffic moving as particles, neither does it impose restriction on road conditions or road tributaries and distributaries. The model is validated using traffic simulator and tested on real road network.
traffic classification hidden markov model spatial-temporal interest points traffic surveillance spatial interest points traffic prediction