Dynamic multi-channel feature dictionaries for robust object tracking
Authors
Karanam, Srikrishna
ORCID
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Other Contributors
Radke, Richard J., 1974-
Stewart, Charles V.
Wang, Meng
Stewart, Charles V.
Wang, Meng
Issue Date
2014-12
Keywords
Electrical engineering
Degree
MS
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Current state-of-the-art trackers use low-resolution image intensity features as part of object appearance modeling. Such features often fail to capture sufficient visual information about the object, and ultimately drift away. In our work, we employ visually richer representation schemes to model the appearance of the object. Specifically, we construct multi-channel feature dictionaries using image intensity, normalized gradient magnitude, and quantized gradient orientation information. To further mitigate the tracking drift problem, we take into account the dynamics of the past state vectors of the object, and propose a novel dynamic adaptive state transition model. We also demonstrate the computational tractability of using richer appearance modeling schemes by adaptively pruning candidate particles during each sampling step, and using a fast augmented Lagrangian technique to solve the associated optimization problem.
Description
December 2014
School of Engineering
School of Engineering
Department
Dept. of Electrical, Computer, and Systems Engineering
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
Access
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