Illuminating the Bias in Pedestrian Detection
Published In
2023 IEEE International Symposium on Multimedia (ISM)
Document Type
Citation
Publication Date
12-1-2023
Abstract
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.
Rights
© Copyright 2024 IEEE - All rights reserved.
Locate the Document
DOI
10.1109/ISM59092.2023.00020
Persistent Identifier
https://archives.pdx.edu/ds/psu/41787
Publisher
IEEE
Citation Details
Althoupety, A., Wang, L.-Y., Feng, W.-C., & Rekabdar, B. (2023, December 11). Illuminating the Bias in Pedestrian Detection. 2023 IEEE International Symposium on Multimedia (ISM). https://doi.org/10.1109/ism59092.2023.00020