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|Title:||Multi-channel correlation filters|
|Keywords:||correlation filter learning|
multi channel features
|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|
|Appears in Collections:||Staff Publications|
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