In order to overcome the effect of shadow in process of vehicles tracking under stationary camera

we present an improved codebook model detection algorithm. This method deal with vehicles sequences directly in the YUV Color Space

and the sampled background values are quantized into codebooks. Input pixel values of new frame are compared with the codebooks to identifying foreground areas. The Kalman Prediction method is used for vehicles tracking which can deal with occlusion. Experiments show that this algorithm can detect moving objects in complex traffic scenes effectively and rapidly. The proposed method can handle shadows

highlights

occlusion and the change of background all of which make this method efficient in both computation and the needs of real-time tracking.