Zaffar, Mubariz and Ehsan, Shoaib and Milford, Michael and McDonald-Maier, Klaus (2020) CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments. IEEE Robotics and Automation Letters, 5 (2). pp. 1835-1842. DOI https://doi.org/10.1109/lra.2020.2969917
Zaffar, Mubariz and Ehsan, Shoaib and Milford, Michael and McDonald-Maier, Klaus (2020) CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments. IEEE Robotics and Automation Letters, 5 (2). pp. 1835-1842. DOI https://doi.org/10.1109/lra.2020.2969917
Zaffar, Mubariz and Ehsan, Shoaib and Milford, Michael and McDonald-Maier, Klaus (2020) CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments. IEEE Robotics and Automation Letters, 5 (2). pp. 1835-1842. DOI https://doi.org/10.1109/lra.2020.2969917
Abstract
This letter presents a novel, compute-efficient and training-free approach based on Histogram-of-Oriented-Gradients (HOG) descriptor for achieving state-of-the-art performance-per-compute-unit in Visual Place Recognition (VPR). The inspiration for this approach (namely CoHOG) is based on the convolutional scanning and regions-based feature extraction employed by Convolutional Neural Networks (CNNs). By using image entropy to extract regions-of-interest (ROI) and regional-convolutional descriptor matching, our technique performs successful place recognition in changing environments. We use viewpoint- and appearance-variant public VPR datasets to report this matching performance, at lower RAM commitment, zero training requirements and 20 times lesser feature encoding time compared to state-of-the-art neural networks. We also discuss the image retrieval time of CoHOG and the effect of CoHOG's parametric variation on its place matching performance and encoding time.
Item Type: | Article |
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Uncontrolled Keywords: | SLAM; visual place recognition; autonomous vehicle navigation; computer vision for automation |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 27 Feb 2020 09:58 |
Last Modified: | 30 Oct 2024 16:55 |
URI: | http://repository.essex.ac.uk/id/eprint/26957 |
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Licence: Creative Commons: Attribution 3.0