Accurate estimation of optical flow for fully automated tracking of moving-objects within video streams

This paper proposes a new approach to accurately estimate optical-flow for fully automated tracking of moving-objects within video streams. This approach consists of an occlusion-killer algorithm and template matching using segmented regions, which is performed by a skip-labeling algorithm. The skip-labeling algorithm can be used to segment an image into integrated regions of the same feature. The occlusion-killer algorithm is executed using the difference between the results of the forward and backward template matching in segmented frames. The occlusion-killer algorithm can be used to accurately estimate optical-flow in the neighboring regions of a moving-object on the background. A well-known optical-flow estimation algorithm for the block matching has problems of insufficient accuracy is limited due to block size, aperture, and occlusion. A new optical-flow estimation algorithm provides the solutions to these problems. Experiments are carried out to approve this new optical-flow estimation method can be applied to the objects of nonlinear movement.