The Community for Technology Leaders
Green Image
Issue No. 10 - Oct. (2013 vol. 35)
ISSN: 0162-8828
pp: 2427-2441
Junseok Kwon , Dept. of Electr. Eng. & Comput. Sci., Seoul Nat. Univ., Seoul, South Korea
Kyoung Mu Lee , Dept. of Electr. Eng. & Comput. Sci., Seoul Nat. Univ., Seoul, South Korea
A novel tracking algorithm is proposed for targets with drastically changing geometric appearances over time. To track such objects, we develop a local patch-based appearance model and provide an efficient online updating scheme that adaptively changes the topology between patches. In the online update process, the robustness of each patch is determined by analyzing the likelihood landscape of the patch. Based on this robustness measure, the proposed method selects the best feature for each patch and modifies the patch by moving, deleting, or newly adding it over time. Moreover, a rough object segmentation result is integrated into the proposed appearance model to further enhance it. The proposed framework easily obtains segmentation results because the local patches in the model serve as good seeds for the semi-supervised segmentation task. To solve the complexity problem attributable to the large number of patches, the Basin Hopping (BH) sampling method is introduced into the tracking framework. The BH sampling method significantly reduces computational complexity with the help of a deterministic local optimizer. Thus, the proposed appearance model could utilize a sufficient number of patches. The experimental results show that the present approach could track objects with drastically changing geometric appearance accurately and robustly.
Target tracking, Topology, Robustness, Computational modeling, Adaptation models, Sampling methods, Proposals,likelihood landscape analysis, Object tracking, nonrigid object, local patch-based appearance model, Basin Hopping Sampling, Markov Chain Monte Carlo
Junseok Kwon, Kyoung Mu Lee, "Highly Nonrigid Object Tracking via Patch-Based Dynamic Appearance Modeling", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 2427-2441, Oct. 2013, doi:10.1109/TPAMI.2013.32
288 ms
(Ver 3.1 (10032016))