Tracking by sequential Bayesian filtering relies on a graphical model with temporally ordered linear structure based on temporal smoothness assumption. This framework is convenient to propagate the posterior through the first-order Markov chain. However, density propagation from a single immediately preceding frame may be unreliable especially in challenging situations such as abrupt appearance changes, fast motion, occlusion, and so on. We propose a visual tracking algorithm based on more general graphical models, where multiple previous frames contribute to computing the posterior in the current frame and edges between frames are created upon inter-frame trackability. Such data-driven graphical model reflects sequence structures as well as target characteristics, and is more desirable to implement a robust tracking algorithm. The proposed tracking algorithm runs online and achieves outstanding performance with respect to the state-of-the-art trackers. We illustrate quantitative and qualitative performance of our algorithm in all the sequences in tracking benchmark and other challenging videos. © 2014 Springer International Publishing.
CITATION STYLE
Nam, H., Hong, S., & Han, B. (2014). Online graph-based tracking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8693 LNCS, pp. 112–126). Springer Verlag. https://doi.org/10.1007/978-3-319-10602-1_8
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