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arXiv · 2110.05876

Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar

Lorenzo Servadei·Huawei Sun·Julius Ott·Michael Stephan·Souvik Hazra·Thomas Stadelmayer·Daniela Sanchez Lopera·Robert Wille·Avik Santra
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Abstract

In this paper, we introduce the Label-Aware Ranked loss, a novel metric loss function. Compared to the state-of-the-art Deep Metric Learning losses, this function takes advantage of the ranked ordering of the labels in regression problems. To this end, we first show that the loss minimises when datapoints of different labels are ranked and laid at uniform angles between each other in the embedding space. Then, to measure its performance, we apply the proposed loss on a regression task of people counting with a short-range radar in a challenging scenario, namely a vehicle cabin. The introduced approach improves the accuracy as well as the neighboring labels accuracy up to 83.0% and 99.9%: An increase of 6.7%and 2.1% on state-of-the-art methods, respectively.

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Lorenzo Servadei, Huawei Sun, Julius Ott, Michael Stephan, Souvik Hazra, Thomas Stadelmayer, Daniela Sanchez Lopera, Robert Wille, Avik Santra. 2021-10-12. Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar. https://doi.org/10.1109/icassp43922.2022.9747621

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