International Journal of Engineering and Technology

Object tracking is a procedure of movement estimation in computer vision application we present a combined multiple object tracking technique for a video. A video is a frame by frame sequence of images. Optical flow is a flexible representation of visual motion that is particularly suitable for computers for analyzing digital images. In this work the Horn & Schunck method is used to find the optical flow vectors which in turn pave a way for the detection and tracking of the single moving object in a video. Kalman filter removes the noise that effects a background subtracted image and predicts the position of an object accurately. A combination of Optical flow and Kalman filter method is designed in order to attain an accurate object tracking system. The accuracy of occluded object in dynamic background is promising compared to simple background subtraction. The experiments are conducted on different videos to prove the efficiency and the results are discussed.