Contour-based object tracking in video scenes through optical flow and gabor features

While many algorithms have been proposed for object tracking with demonstrated success, a crucial problem still persists which is to improve the performance of non-rigid object structures. This paper presents a new, efficient algorithm for Movement Estimation and object tracking in video scenes using Optical Flow and Gabor Features Based Contour Model. The target motion detection is done based on optical flow method to calculate the flow field, according to the optical flow distribution characteristics. Once the flow field has been determined it is used for motion analysis and the Expectation Maximization Based Effective Gaussian Mixture Model (EMEGMM) algorithm based background subtraction is performed to obtain the foreground pixels. With this method complete motion, shape and Gabor features are estimated. The extracted features are classified using Adaboost classifier for effectively handling the region of interest. Then contour based object tracking is carried out by locating the object region in every frame through the object model created by the previous frames. The object shapes are considered as boundary silhouettes and the tracking results obtained are updated dynamically in the video frames. Experimental outcomes validate that our proposed method runs faster and is more accurate, when compared to the several state-of-the-art tracking methods.