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Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model
April 2013 (vol. 35 no. 4)
pp. 941-953
L. Čehovin, Fac. of Comput. & Inf. Sci., Univ. of Ljubljana, Ljubljana, Slovenia
M. Kristan, Fac. of Electr. Eng., Univ. of Ljubljana, Ljubljana, Slovenia
A. Leonardis, Centre for Comput. Neurosci. & Cognitive Robot., Univ. of Birmingham, Birmingham, UK
This paper addresses the problem of tracking objects which undergo rapid and significant appearance changes. We propose a novel coupled-layer visual model that combines the target's global and local appearance by interlacing two layers. The local layer in this model is a set of local patches that geometrically constrain the changes in the target's appearance. This layer probabilistically adapts to the target's geometric deformation, while its structure is updated by removing and adding the local patches. The addition of these patches is constrained by the global layer that probabilistically models the target's global visual properties, such as color, shape, and apparent local motion. The global visual properties are updated during tracking using the stable patches from the local layer. By this coupled constraint paradigm between the adaptation of the global and the local layer, we achieve a more robust tracking through significant appearance changes. We experimentally compare our tracker to 11 state-of-the-art trackers. The experimental results on challenging sequences confirm that our tracker outperforms the related trackers in many cases by having a smaller failure rate as well as better accuracy. Furthermore, the parameter analysis shows that our tracker is stable over a range of parameter values.
Index Terms:
object tracking,geometry,geometric deformation,robust visual tracking,adaptive coupled-layer visual model,object tracking,Visualization,Target tracking,Adaptation models,Robustness,Computational modeling,Shape,tracking,Image processing and computer vision
L. Čehovin, M. Kristan, A. Leonardis, "Robust Visual Tracking Using an Adaptive Coupled-Layer Visual Model," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 4, pp. 941-953, April 2013, doi:10.1109/TPAMI.2012.145
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