Handling occlusion in optical flow algorithms for object tracking

Authors:
Eduardo Parrilla;Damián Ginestar;José Luis Hueso;Jaime Riera;Juan Ramón Torregrosa
Affiliations:
Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Camino de Vera s/n, Edificio 8G, 46022 Valencia, Spain;Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Camino de Vera s/n, Edificio 8G, 46022 Valencia, Spain;Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Camino de Vera s/n, Edificio 8G, 46022 Valencia, Spain;Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Camino de Vera s/n, Edificio 8G, 46022 Valencia, Spain;Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Camino de Vera s/n, Edificio 8G, 46022 Valencia, Spain
Venue:
Computers & Mathematics with Applications
Year:
2008

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Abstract

In this paper, we study simple algorithms for tracking objects in a video sequence, based on the selection of landmark points representative of the moving objects in the first frame of the sequence to be analyzed. The movement of these points is estimated using a sparse optical-flow method. Methods of this kind are fast, but they are not very robust. Particularly, they are not able to handle the occlusion of the moving objects in the video. To improve the performance of optical flow-based methods, we propose the use of adaptive filters and neural networks to predict the expected instantaneous velocities of the objects, using the predicted velocities as indicators of the performance of the tracking algorithm. The efficiency of these strategies in handling occlusion problems are tested with a set of synthetic and real video sequences.