Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark
Abstract
:1. Introduction
- We formulate a novel task of identity-preserved human posture detection in thermal images, which underpins various applications where privacy matters and which may also draw attention to more informative object detection other than identification and localization.
- We present the IPHPDT dataset, which is the first benchmark dedicated to identity-preserved human posture detection in thermal images.
- We develop three baseline detectors based on three state-of-the-art detectors, i.e., YOLOF, YOLOX, and TOOD, to facilitate and encourage further research on IPHPDT.
2. Related Work
2.1. Traditional Methods of Human Detection in Infrared Thermal Images
2.2. Deep Learning Methods for Human Detection Based on Infrared Thermal Images
2.3. Human Pose Estimation
3. Benchmark for Detecting Posture of Human
3.1. IPHPDT Collection
3.2. Annotation
- category: person.
- bounding box: a bounding box with axis-alignment around the visible human in the image.
- human posture: one of standing, sitting, lying, and bending.
3.3. Image Processing
3.4. Dataset Statistics
4. Baseline Detectors for Detecting Human Posture in Thermal Images
4.1. IPH-YOLOF
4.2. IPH-YOLOX
4.3. IPH-TOOD
5. Evaluation
5.1. Evaluation Metrics
5.2. Evaluation Results
5.3. Ablation Study
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Method | Year | Dataset | Learning Method | Supervisio Method | YOLO | Attention | FPN | Posture Prediction Head |
---|---|---|---|---|---|---|---|---|
[40] | 2013 | OSU-T | Traditional learning | Supervised | × | √ | × | × |
[50] | 2016 | Non-public | × | × | × | × | ||
[39] | 2019 | OSU IMS DIP | × | × | × | × | ||
[51] | 2018 | Non-public | Traditional learning | Semi- supervised | × | √ | × | × |
[52] | 2021 | IRPSRL MS COCO | Deep learning | × | × | × | × | |
[53] | 2018 | Non-public | Deep learning | Unsupervised | × | × | × | × |
[45] | 2020 | Non-public | √ | × | √ | × | ||
[41] | 2017 | OSU-T OSU-CT LSI KAIST | Deep learning | Supervised | × | × | × | × |
[47] | 2021 | OSU-T | √ | √ | √ | × | ||
[54] | 2021 | MPII-HPD AI-CD | × | √ | × | × | ||
IPH-YOLOF | 2022 | IPHPTD | √ | √ | × | √ | ||
IPH-YOLOX | 2022 | IPHPTD | √ | √ | √ | √ | ||
IPH-TOOD | 2022 | IPHPTD | × | √ | √ | √ |
Dataset | Train Set | Valid Set | Test Set |
---|---|---|---|
IPHD | 84,818 | 12,974 | 15,115 |
IPHPDT | 62,010 | - | 13,267 |
{,,}@0.5 | {,,}@0.75 | {,,} | |
---|---|---|---|
IPH-YOLOF | (0.944,0.833,0.867) | (0.848,0.768,0.834) | (0.706,0.630,0.692) |
IPH-YOLOX | (0.955,0.804,0.836) | (0.863,0.737,0.771) | (0.737,0.625,0.677) |
IPH-TOOD | (0.935,0.826,0.863) | (0.850,0.771,0.836) | (0.719,0.643,0.704) |
Standing | Sitting | Lying | Bending | |
---|---|---|---|---|
(IPH-YOLOF) | 0.723 | 0.666 | 0.720 | 0.665 |
(IPH-YOLOX) | 0.743 | 0.625 | 0.721 | 0.619 |
(IPH-TOOD) | 0.737 | 0.652 | 0.725 | 0.695 |
Backbone | {,,}@0.5 | {,,}@0.75 | {,,} |
---|---|---|---|
ResNet-18 | (0.912,0.771,0.804) | (0.797,0.678,0.744) | (0.670,0.569,0.628) |
ResNet-34 | (0.924,0.776,0.831) | (0.820,0.694,0.777) | (0.689,0.583,0.655) |
ResNet-50 | (0.935,0.826,0.863) | (0.850,0.771,0.836) | (0.719,0.643,0.704) |
ResNet-101 | (0.925,0.792,0.835) | (0.839,0.728,0.798) | (0.711,0.614,0.680) |
ResNet-152 | (0.933,0.796,0.840) | (0.833,0.726,0.797) | (0.706,0.614,0.687) |
Frozen Stages | {,,}@0.5 | {,,}@0.75 | {,,} |
---|---|---|---|
fs_−1 | (0.924,0.791,0.835) | (0.824,0.723,0.794) | (0.690,0.603,0.666) |
fs_0 | (0.932,0.811,0.851) | (0.847,0.751,0.817) | (0.715,0.630,0.692) |
fs_1 | (0.935,0.826,0.863) | (0.850,0.771,0.836) | (0.719,0.643,0.704) |
fs_2 | (0.934,0.804,0.847) | (0.847,0.744,0.814) | (0.714,0.622,0.686) |
fs_3 | (0.923,0.773,0.818) | (0.820,0.695,0.769) | (0.686,0.580,0.645) |
fs_4 | (0.902,0.718,0.740) | (0.785,0.622,0.678) | (0.662,0.523,0.574) |
{,,}@0.5 | {,,}@0.75 | {,,} | |
---|---|---|---|
0.2 | (0.934,0.805,0.843) | (0.848,0.751,0.815) | (0.716,0.622,0.682) |
0.4 | (0.935,0.827,0.860) | (0.846,0.764,0.825) | (0.715,0.640,0.696) |
0.6 | (0.935,0.828,0.862) | (0.848,0.766,0.829) | (0.716,0.643,0.700) |
0.8 | (0.935,0.822,0.857) | (0.850,0.768,0.833) | (0.720,0.643,0.702) |
1.0 | (0.935,0.826,0.863) | (0.850,0.771,0.836) | (0.719,0.643,0.704) |
1.2 | (0.936,0.811,0.853) | (0.848,0.753,0.822) | (0.717,0.630,0.693) |
1.4 | (0.935,0.820,0.857) | (0.850,0.769,0.833) | (0.717,0.641,0.701) |
1.6 | (0.937,0.805,0.852) | (0.850,0.756,0.830) | (0.720,0.632,0.698) |
1.8 | (0.935,0.815,0.855) | (0.849,0.754,0.824) | (0.718,0.630,0.693) |
2.0 | (0.935,0.814,0.853) | (0.849,0.759,0.825) | (0.719,0.634,0.695) |
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Guo, Y.; Chen, Y.; Deng, J.; Li, S.; Zhou, H. Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark. Sensors 2023, 23, 92. https://doi.org/10.3390/s23010092
Guo Y, Chen Y, Deng J, Li S, Zhou H. Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark. Sensors. 2023; 23(1):92. https://doi.org/10.3390/s23010092
Chicago/Turabian StyleGuo, Yongping, Ying Chen, Jianzhi Deng, Shuiwang Li, and Hui Zhou. 2023. "Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark" Sensors 23, no. 1: 92. https://doi.org/10.3390/s23010092
APA StyleGuo, Y., Chen, Y., Deng, J., Li, S., & Zhou, H. (2023). Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark. Sensors, 23(1), 92. https://doi.org/10.3390/s23010092