CoHOG: A Light-Weight, Compute-Efficient, and Training-Free Visual Place Recognition Technique for Changing Environments

Zaffar, Mubariz, Ehsan, Shoaib, 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), 1835-1842. (doi:10.1109/LRA.2020.2969917).

Abstract

This letter presents a novel, compute-efficient and training-free approach based on Histogram-of-OrientedGradients (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 viewpointand 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.

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Contributors

Author: Shoaib Ehsan

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