Human Motion tracking using Gaussian Mixture Method and Beta-Likelihood Matching

Video surveillance is widely used to monitor the place which needs constant security such as Banks, Shopping Malls, Highways, crowded public places, country borders etc. The major disputes include the complex motion behaviours of different human objects, complex scenes with numerous targets, detection of change in human motion. The objective of this paper is to develop a visual detection and tracking system of observing moving objects. We propose the GMM-Likelihood matching Method of tracking algorithm which integrates the adaptive best background detection, data association, adding new hypothesis update kalman measurement, and linear assignment problem to minimise the cost of observation of tracking. The experimental result shows that the active background can be extracted accurately and expeditiously, the algorithm is more robust, and can be utilized in the real time tracking applications. keywords : Real-time visual tracking, Active background estimation, Activity modelling, Data association, Video surveillance and monitoring, Gaussian mixture model, negative log likelihood matching, Kalman filter, Linear Assignment problem.