Multi-channel Correlation Filters
2013
269 citations
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.
Keywords
Computer scienceChannel (broadcasting)Convolution (computer science)Artificial intelligenceDetectorFilter (signal processing)Computer visionObject detectionSpatial correlationPattern recognition (psychology)TelecommunicationsArtificial neural network
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Publication Info
- Year
- 2013
- Type
- article
- Citations
- 269
- Access
- Closed
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Cite This
Hamed Kiani Galoogahi,
Terence Sim,
Simon Lucey
(2013).
Multi-channel Correlation Filters.
.
https://doi.org/10.1109/iccv.2013.381
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- DOI
- 10.1109/iccv.2013.381