Intelligent activity detection techniques for advanced HD video surveillance systems

Video surveillance systems are becoming increasingly popular due to the emergence of high-speed wireless Internet (such as WiMax and LTE), bandwidth-efficient video compression schemes (such as H.264), and low-cost (and high-resolution) IP video cameras. This paper presents two applications of an advanced surveillance system, specifically in suspicious activity detection and human fall detection, for both indoor and outdoor environments. The implemented prototype captures and analyzes live high-definition (HD) video that is streamed from a remote camera. We will show that by combining the strengths of ellipse modeling and shadow removal, and other novel algorithms, the false alarms in the detection can be significantly reduced.