Visual Tracking on Riemannian Space Using Updated Standard Deviation Based Model
نویسندگان
چکیده
ISSN: 2186-1390 http://www.cennser.org/IJCVSP Abstract Object tracking using appearance based modeling from non stationary camera is one of the key aspect of visual tracking. While most of the existing algorithms are able to track objects well in controlled environments, those methods usually fail to track a longer sequence of trajectory in the presence of significant variation of the objects appearance or surrounding illumination. In this paper, we propose a new simple standard deviation based model updated method for tracking a longer sequence of trajectory for the target object. Non singular covariance based feature subspace is constructed for each candidate image region that lie on riemannian space. This feature subspace is updated by adding the vector mean difference of standard deviation between the referenced object and the detected objects, to each observation vector of the referenced model. The resultant covariance structure of this updated target reference model can be used for tracking the next sequence video frame. In the proposed model, also we use the kalman filtering for effectively handle the background clutter and temporary occlusion. Simulation result shows the current method is robust for real time tracking.
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