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Moreover, the implementation of the proposed algorithm on a prototype embedded system, based on an off-the-shelf multimedia processor, is discussed in this chapter. This prototype is used as a test-bench for comparison of the different background subtraction algorithms, in terms of segmentation quality performance and computational efficiency. 2. 1 Basic Sigma-Delta algorithm The basic sigma-delta background estimation algorithm provides a recursive computation of a valid background model of the scene assuming that, at the pixel level, the background intensities are present most of the time.

Another implicit parameter in the algorithm is the updating period of the statistics, which depends on the frame rate and the number of grey levels. This updating period can be modified by performing the loop processing every P frames, instead of every frame. The same algorithm computes the detection image or detection mask, Dt. This binary image highlights pixels belonging to the detected foreground objects (1-valued 43 Computer Vision Techniques for Background Modelling in Urban Traffic Monitoring pixels) in contrast to the stationary background pixels (0-valued pixels).

The subsequent reconstruction of the series of volume and occupancy in the Phase-space using the mutual information criterion (Fraser & Swinney 1986) and the false nearest algorithm (Kennel et al. 1992) indicate that both volume and occupancy can be determined by a vector with time delay that equals to 1 and dimension that equals to 5. Based on the above results, the recursive prediction techniques are set to have a look-back temporal window of 1-hour to retrieve and evaluate the traffic flow patterns.

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