Human detection and tracking using image segmentation and Kalman filter

Human detection and tracking is one of the most popular areas of video processing and the essential requirement of any surveillance system. In this paper we have used image segmentation technique for human detection and Kalman filter with two dimension constant velocity model for human tracking. The Kalman filter is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean square error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. This method tracks individual pedestrians as they pass through the field of vision of the camera, and uses vision algorithms to classify the motion and activities of each pedestrian. The tracking is accomplished through the development of a position and velocity path characteristic for each pedestrian using a Kalman filter. With this information, the system can bring the incident to the attention of human security personnel. In future applications, this system could alert authorities if a pedestrian displays suspicious behavior such as: entering a secure area, running or moving erratically, loitering or moving against traffic, or dropping a bag or other items.