CONDENSATION—Conditional Density Propagation forVisual Tracking
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This work proposes an optical-flow based feature tracking that is combined with region covariance matrix for dealing with tracking of an object undergoing considerable occlusions. The object is tracked using a set of key-points. The key-points are tracked via a computationally inexpensive optical flow algorithm. If the occlusion of the feature is detected the algorithm calculates the covariance matrix inside a region, which is located at the feature's position just before the occlusion. The region covariance matrix is then used to detect the ending of the feature occlusion. This is achieved via comparing the covariance matrix based similarity measures in some window surrounding the occluded key-point. The outliers that arise in the optical flow at the boundary of the objects are excluded using RANSAC and affine transformation. Experimental results that were obtained on freely available image sequences show the feasibility of our approach to perform tracking of objects undergoing considerable occlusions. The resulting algorithm can cope with occlusions of faces as well as objects of similar colors and shapes.