Label-Aware Ranked Loss for robust People Counting using Automotive in-cabin Radar
This work addresses the incremental challenge of robust people counting for automotive safety applications.
The paper tackles the problem of people counting in vehicle cabins using radar by introducing the Label-Aware Ranked loss, a novel metric loss function that leverages label ordering in regression, resulting in accuracy improvements of up to 83.0% and 99.9% for accuracy and neighboring labels accuracy, with increases of 6.7% and 2.1% over state-of-the-art methods.
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.