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With the acquisition of an image, the first step is to distinguish objects of interest from the background. In surveillance applications, those objects of interest are usually humans. Their various shapes and different motions, including walking, jumping, bending down, and so forth, represent significant challenges in the extraction of foreground pixels from the image.

An important feature of crowd surveillance is the estimation of the number of persons in the monitored scene. People counting is especially important for crowd modeling as it not only gives a direct estimation of the current situation in the field of view but also contributes to other tasks including behavior analysis and crowd simulation.

In the surveillance of public areas, human groups should be the major targets monitored because of their highly sociable character. In groups, the behavior of individuals usually depends on that of others. Hence, group behavior can explain a large number of phenomena, including overcrowding, chaos, stampedes (as a result of a terrorist attack such as a bomb explosion), fights, and riots.

In many cases, we cannot obtain the background image easily, and if the occlusion is serious, then it is difficult to segment masses of blobs into single blobs.We therefore use optical flow to detect abnormal behavior in crowded environments [57], [58].

After successful segmentation and tracking, the temporal-spatial information of each blob sequence can be indexed for further behavior analysis. In this chapter, we elaborate two kinds of approaches for the behavior analysis of individuals: learningbased and rule-based.

After foreground extraction, if nonsignificant merging occurs among people, then segmentation is necessary, and the merged blobs can be split into separate ones equal in number to the number of people in the merged blob. Thereafter, all of the blobs can be indexed and tracked for motion and behavior analysis.

In the previous chapter, we presented behavior analysis, which is based on dynamic information, including background extraction based on a series of frames, blob detection based on two consecutive frames, and so forth. However, for intelligent surveillance, we can also explore static information, i.e., facial images. Facial detection is straightforward as it does not depend on whether or not background information is extractable. Hence, it is useful in highly cluttered environments, with the only requirement being sufficient image resolution of the image.