Just Accepted
Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions
Accepted on 27 September 2024
Abstract
Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.
References
[1]
Amran Bhuiyan, Alessandro Perina, and Vittorio Murino. 2015. Exploiting multiple detections to learn robust brightness transfer functions in re-identification systems. In 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2329–2333.
[2]
Henri Bouma, Sander Borsboom, Richard JM den Hollander, Sander H Landsmeer, and Marcel Worring. 2012. Re-identification of persons in multi-camera surveillance under varying viewpoints and illumination. In Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense XI, Vol. 8359. SPIE, 161–170.
[3]
Zhiyi Cheng, Qi Dong, Shaogang Gong, and Xiatian Zhu. 2020. Inter-task association critic for cross-resolution person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2605–2615.
[4]
Seokeon Choi, Sumin Lee, Youngeun Kim, Taekyung Kim, and Changick Kim. 2020. Hi-CMD: Hierarchical cross-modality disentanglement for visible-infrared person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10257–10266.
[5]
Weijian Deng, Liang Zheng, Qixiang Ye, Guoliang Kang, Yi Yang, and Jianbin Jiao. 2018. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 994–1003.
[6]
Chanho Eom, Wonkyung Lee, Geon Lee, and Bumsub Ham. 2021. Disentangled representations for short-term and long-term person re-identification. IEEE transactions on pattern analysis and machine intelligence 44, 12 (2021), 8975–8991.
[7]
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. Generative adversarial networks. Commun. ACM 63, 11 (2020), 139–144.
[8]
Lingxiao He, Jian Liang, Haiqing Li, and Zhenan Sun. 2018. Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7073–7082.
[9]
Tianyu He, Xu Shen, Jianqiang Huang, Zhibo Chen, and Xian-Sheng Hua. 2021. Partial person re-identification with part-part correspondence learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 9105–9115.
[10]
Yukun Huang, Zheng-Jun Zha, Xueyang Fu, and Wei Zhang. 2019. Illumination-invariant person re-identification. In Proceedings of the 27th ACM international conference on multimedia. 365–373.
[11]
Shradha Jaiswal and Dinesh Kumar Vishwakarma. 2019. State-of-the-arts person re-identification using deep learning. In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 238–243.
[12]
Igor Kviatkovsky, Amit Adam, and Ehud Rivlin. 2012. Color invariants for person reidentification. IEEE Transactions on pattern analysis and machine intelligence 35, 7 (2012), 1622–1634.
[13]
Jiawei Liu, Zheng-Jun Zha, Di Chen, Richang Hong, and Meng Wang. 2019. Adaptive transfer network for cross-domain person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 7202–7211.
[14]
Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, and Wei Jiang. 2019. Bag of tricks and a strong baseline for deep person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 0–0.
[15]
Fei Ma, Xiaoke Zhu, Xinyu Zhang, Liang Yang, Mei Zuo, and Xiao-Yuan Jing. 2019. Low illumination person re-identification. Multimedia Tools and Applications 78 (2019), 337–362.
[16]
Liqian Ma, Xu Jia, Qianru Sun, Bernt Schiele, Tinne Tuytelaars, and Luc Van Gool. 2017. Pose guided person image generation. Advances in neural information processing systems 30 (2017).
[17]
M Saquib Sarfraz, Arne Schumann, Andreas Eberle, and Rainer Stiefelhagen. 2018. A pose-sensitive embedding for person re-identification with expanded cross neighborhood re-ranking. In Proceedings of the IEEE conference on computer vision and pattern recognition. 420–429.
[18]
Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. 2018. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In Proceedings of the European conference on computer vision (ECCV). 480–496.
[19]
Zhihong Sun, Jun Chen, Chao Liang, Weijian Ruan, and Mithun Mukherjee. 2021. A Survey of Multiple Pedestrian Tracking Based on Tracking-by-Detection Framework. IEEE Transactions on Circuits and Systems for Video Technology 31, 5 (2021), 1819–1833.
[20]
Zhihong Sun, Jun Chen, Mithun Mukherjee, Chao Liang, and Weijian Ruan. 2021. Online Multiple Object Tracking Based on Fusing Global and Partial Features. Neurocomputing 456 (2021), 513–521.
[21]
Zhihong Sun, Jun Chen, Mithun Mukherjee, Weijian Ruan, Chao Liang, Yi Yu, and Dan Zhang. 2021. Long-short Term Prediction for Occluded Multiple Object Tracking. In 2021 IEEE Global Communications Conference (GLOBECOM). 1–6.
[22]
Guan’an Wang, Shuo Yang, Huanyu Liu, Zhicheng Wang, Yang Yang, Shuliang Wang, Gang Yu, Erjin Zhou, and Jian Sun. 2020. High-order information matters: Learning relation and topology for occluded person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 6449–6458.
[23]
Yimin Wang, Ruimin Hu, Chao Liang, Chunjie Zhang, and Qingming Leng. 2014. Camera compensation using a feature projection matrix for person reidentification. IEEE transactions on circuits and systems for video technology 24, 8 (2014), 1350–1361.
[24]
Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. 2018. Person transfer gan to bridge domain gap for person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 79–88.
[25]
Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi. 2021. Deep learning for person re-identification: A survey and outlook. IEEE transactions on pattern analysis and machine intelligence 44, 6 (2021), 2872–2893.
[26]
Hui Ying, He Wang, Tianjia Shao, Yin Yang, and Kun Zhou. 2021. Unsupervised Image Generation with Infinite Generative Adversarial Networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 14284–14293.
[27]
Tuo Yu, Haiming Jin, Wai-Tian Tan, and Klara Nahrstedt. 2018. SKEPRID: Pose and illumination change-resistant skeleton-based person re-identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 14, 4 (2018), 1–24.
[28]
Zelong Zeng, Zhixiang Wang, Zheng Wang, Yinqiang Zheng, Yung-Yu Chuang, and Shin’ichi Satoh. 2020. Illumination-adaptive person re-identification. IEEE Transactions on Multimedia 22, 12 (2020), 3064–3074.
[29]
Guoqing Zhang, Zhiyuan Luo, Yuhao Chen, Yuhui Zheng, and Weisi Lin. 2022. Illumination unification for person re-identification. IEEE Transactions on Circuits and Systems for Video Technology 32, 10 (2022), 6766–6777.
[30]
Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. 2015. Scalable person re-identification: A benchmark. In Proceedings of the IEEE international conference on computer vision. 1116–1124.
[31]
Wei-Shi Zheng, Xiang Li, Tao Xiang, Shengcai Liao, Jianhuang Lai, and Shaogang Gong. 2015. Partial person re-identification. In Proceedings of the IEEE international conference on computer vision. 4678–4686.
[32]
Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, and Jan Kautz. 2019. Joint discriminative and generative learning for person re-identification. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2138–2147.
[33]
Zhedong Zheng, Liang Zheng, and Yi Yang. 2017. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In Proceedings of the IEEE international conference on computer vision. 3754–3762.
[34]
Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, and Yi Yang. 2018. Camera style adaptation for person re-identification. In Proceedings of the IEEE conference on computer vision and pattern recognition. 5157–5166.
[35]
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision. 2223–2232.
[36]
Zijie Zhuang, Longhui Wei, Lingxi Xie, Tianyu Zhang, Hengheng Zhang, Haozhe Wu, Haizhou Ai, and Qi Tian. 2020. Rethinking the distribution gap of person re-identification with camera-based batch normalization. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XII 16. Springer, 140–157.
Index Terms
- Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions
Index terms have been assigned to the content through auto-classification.
Recommendations
Local illumination environments for direct lighting acceleration
EGRW '02: Proceedings of the 13th Eurographics workshop on RenderingComputing high-quality direct illumination in scenes with many lights is an open area of research. This paper presents a world-space caching mechanism called local illumination environments that enables interactive direct illumination in complex scenes ...
Real-time BRDF editing in complex lighting
Current systems for editing BRDFs typically allow users to adjust analytic parameters while visualizing the results in a simplified setting (e.g. unshadowed point light). This paper describes a real-time rendering system that enables interactive edits ...
Splatting indirect illumination
I3D '06: Proceedings of the 2006 symposium on Interactive 3D graphics and gamesIn this paper we present a novel method for plausible real-time rendering of indirect illumination effects for diffuse and non-diffuse surfaces. The scene geometry causing indirect illumination is captured by an extended shadow map, as proposed in ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
EISSN:1551-6865
Table of ContentsCopyright © 2024 Copyright held by the owner/author(s).
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Online AM: 15 October 2024
Accepted: 27 September 2024
Revised: 24 September 2024
Received: 10 August 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 60Total Downloads
- Downloads (Last 12 months)60
- Downloads (Last 6 weeks)60
Reflects downloads up to 13 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in