Content-based scalable H.263 video coding for road traffic monitoring based on regularity of video content

For real-time road traffic monitoring, the content-based scalable H.263 video codec was recently proposed. To achieve high compression rate, it was suggested to analyze the activity of the target road-trafic video such that moving objects are coded in different data rates according to their speed. However, the analysis peformed by that approach relies on the variance of the scene change magnitude. It can be inaccurate when the scene is complicated. An alternative approach is proposed in this paper. For the proposed approach, the moving objects, i.e. cars, are first extracted from the steady background. Their activities are then classified as fast or slow by assessing the regularity of their motion. It is achieved by evaluating the zero crossing density of the derivative of the scene change pattern across time. The information is then passed to a modified H.263 encoder to reduce the temporal and spatial redundancies in the video. As compared with the conventional H.263 encoder using for the same application, the proposed system has a 20% increase in compression rate with negligible visual distortion. When comparing with the previous content-based scalable approach, the current approach has a much higher accuracy in activity analysis. The proposed system fully complies with the ITU H.263 standard hence the encoded bit stream is completely comprehensible to the conventional H.263 decoder.