Online single and multiple analysis dictionary learning-based approach for visual object tracking

Divya Sitani; A. Venkata Subramanyam; Angshul Majumdar

doi:10.1117/1.JEI.28.1.013004

12 January 2019 Online single and multiple analysis dictionary learning-based approach for visual object tracking

Divya Sitani, A. Venkata Subramanyam, Angshul Majumdar

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013004 (January 2019). https://doi.org/10.1117/1.JEI.28.1.013004

Abstract

In recent years, various synthesis dictionary learning-based techniques have been applied for the task of visual object tracking. However, there are challenges in the representation ability of the target and the background using a synthesis dictionary. Owing to such challenges, the target in a given video sequence is not tracked very efficiently. We propose an online analysis dictionary learning framework for visual tracking. Our main motivation stems from the fact that analysis dictionary is able to capture significantly more variability in the data as compared to a synthesis dictionary of the same dimensions. We make use of single and multiple analysis dictionaries in our work. In order to leverage the advantage of multiple dictionaries, we adaptively fuse them. To the best of our knowledge, this is the first work that uses analysis dictionary for visual tracking. We use the entire OTB-50 dataset to evaluate the performance of proposed trackers. Our experiments show a significant improvement over existing synthesis dictionary learning approaches. This makes our approach much more preferable over the conventional approaches that use synthesis dictionary learning model for visual tracking.

Citation Download Citation

Divya Sitani, A. Venkata Subramanyam, and Angshul Majumdar "Online single and multiple analysis dictionary learning-based approach for visual object tracking," Journal of Electronic Imaging 28(1), 013004 (12 January 2019). https://doi.org/10.1117/1.JEI.28.1.013004

Received: 2 September 2018; Accepted: 11 December 2018; Published: 12 January 2019

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