Abnormal Motion Detection in Real Time Using Video Surveillance and Body Sensors

This paper presents a method for detecting abnormal motion in real time using a computer vision system. The method is based on the modeling of human body image, which takes into account both orientation and velocity of prominent body parts. A comparative study is made of this method with other existing algorithms based on optical flow and the use of accelerometer body sensors. From the real time experiments conducted in the present work, the developed method is found to be efficient in characterizing human motion and classifying it into basic types such as falling, sitting, and walking. The method uses a Radial Basis Function Network (RBFN) to compute the severity coefficient associated with the type of motion, based on experience. The paper evaluates the various methods and incorporates the advantages of other methods in order to develop a more reliable system for abnormal motion detection.