Video Surveillance and Monitoring is very active area of research in Computer Vision. However, most of the current approaches assume that the observed scene is not crowded, and that reliable tracks of objects are available over longer durations. Therefore, these approaches are not extendable to more challenging surveillance videos of crowded environments like markets, subways, religious festivals, parades, concerts, football matches etc, where tracking of individual objects is very hard, if not impossible. We have proposed a framework, which views the flow of a high density crowd like the flow of a liquid, prompting the use of ideas and techniques often found in the study of hydrodynamics. Therefore, we treat interactions of people in the scene like moving particles in a liquid on three different length scales (macroscopic, mesoscopic, and microscopic); each scale corresponding to one of the three problems: tracking individuals, detection of abnormal behaviors, and segmentation of crowd motion.