Illuminating the Bias in Pedestrian Detection

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2023 IEEE International Symposium on Multimedia (ISM)

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Despite major advancements in state-of-the-art object detectors and low-light image enhancements, nighttime pedestrian detection remains a challenge. One commonly used solution to remedy this domain shift problem is re-training object detectors with extra nighttime scenes which is not only computationally expensive but also not generalizable. In this paper, we explore a new solution and aim to systematically analyze and understand the efficiency of a lightening algorithm on pedestrian detection in low-light conditions. In our analysis, we first explore the effect of normalizing image luminance based on the ground truth bounding boxes to adaptively adjust global image luminance and evaluate its effects on detection performance. Second, unlike general low-light image enhancements that rely on global or local image statistics, we design a pedestrian-luminance-aware lightening algorithm to automatically correct nighttime images luminance so that pedestrians can be more robustly detected. Through extensive experiments, our algorithm not only achieves competitive detection results compared to the baseline on two real-world nighttime datasets but also elevates the confidence score of detected pedestrians.


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