Abstract: A bus video detection method for urban public transport is proposed based on AdaBoost algorithm and window color feature. Firstly, the foreground detection method is used to find the motion of vehicle. This method is the "and" operation of two methods: three frame difference method of filtering and expansion and the mixed Gauss method after filtering, threshold method to remove shadow and expansion processing, whcih classifies vehicles in motion detected by the foreground detection algorithm into public transport vehicles, buses and other passenger vehicles, using the Adaboost algorithm and Haar feature training classifie. Compared with the large bus, the window of a public transport vehicle has obvious color characteristics indicating the bus lines and other information. The edge detection of canny operator and connected domain processing are used to locate the windows, then transfer the windows area into HSV color space, count the ratio of characteristic color pixels from windows area, and compare the ratio with threshold that has been set. If the ratio is larger than threshold, then believe it is a public transport bus, otherwise it is not. The experiments are performed using the traffic flow video that contains bus in a city. Experimental results show that, motion detection algorithm in this article has better adaptation with noise from video sequence than Three-frame differencing method or Gaussian mixture model. The threshold of connected domain processing and canny operator from a lot of video sequence contains bus lead to window positioning accuracy rate of more than 95%. Algorithm of characteristic color of windows can distinguish bus and motor coach effectively and accurately.

Key words: intelligent transportation systems, bus vehicles, video detection, Adaboost algorithm, HSV color space