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.
CITATION STYLE
Zaffar, M., Ehsan, S., Milford, M., & McDonald-Maier, K. (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. https://doi.org/10.1109/LRA.2020.2969917
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