Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing

Date

2017-11-01

Author

Gundogdu, Erhan
Özkan, Huseyin
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel tracking algorithm [ of complexity O(D)] based on a large ensemble of CFB trackers. The ensemble [ of size O(2(D))] is organized over a binary tree (depth D), and learns the target appearance subspaces such that each constituent tracker becomes an expert of a certain appearance. During tracking, the proposed algorithm combines only the appearance-aware relevant experts to produce boosted tracking decisions. Additionally, we propose a versatile spatial windowing technique to enhance the individual expert trackers. For this purpose, spatial windows are learned for target objects as well as the correlation filters and then the windowed regions are processed for more robust correlations. In our extensive experiments on benchmark datasets, we achieve a substantial performance increase by using the proposed tracking algorithm together with the spatial windowing.

Subject Keywords

Software, Computer Graphics and Computer-Aided Design

URI

https://hdl.handle.net/11511/35932

Collections

Department of Electrical and Electronics Engineering, Article