Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2013.381
Title: Multi-channel correlation filters
Authors: Galoogahi, H.K.
Sim, T. 
Lucey, S.
Keywords: correlation filter learning
multi channel features
pattern recognition
Issue Date: 2013
Citation: Galoogahi, H.K., Sim, T., Lucey, S. (2013). Multi-channel correlation filters. Proceedings of the IEEE International Conference on Computer Vision : 3072-3079. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2013.381
Abstract: Modern descriptors like HOG and SIFT are now commonly used in vision for pattern detection within image and video. From a signal processing perspective, this detection process can be efficiently posed as a correlation/convolution between a multi-channel image and a multi-channel detector/filter which results in a single channel response map indicating where the pattern (e.g. object) has occurred. In this paper, we propose a novel framework for learning a multi-channel detector/filter efficiently in the frequency domain, both in terms of training time and memory footprint, which we refer to as a multichannel correlation filter. To demonstrate the effectiveness of our strategy, we evaluate it across a number of visual detection/localization tasks where we: (i) exhibit superior performance to current state of the art correlation filters, and (ii) superior computational and memory efficiencies compared to state of the art spatial detectors. © 2013 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
URI: http://scholarbank.nus.edu.sg/handle/10635/78243
ISBN: 9781479928392
DOI: 10.1109/ICCV.2013.381
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