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Pose Detection of Dead Body in Crime Scene Investigation

Published: 28 February 2024 Publication History

Abstract

This paper explores the potential of real-time object detection in Crime Scene Investigation to assist investigators in determining the cause of death and bringing justice to those who deserve it. The study discusses the critical steps in implementing dead body pose estimation using the custom dataset with YOLOv8, including data collection, model training, fine-tuning, and testing results. However, human expertise and integration are necessary in the subsequent stages of crime scene investigation to enhance the effectiveness of the proposed system. The experiment uses Automatic Mixed Precision (AMP) by bbox and poses estimation, with suitable performance matrices of the bbox. The result shows all class mPA50 at 0.98 of bbox prediction, while the mPA50 of the pose estimation is 0.969. When dealing with small custom datasets, the precision needed for predicting keypoints results in lower performance of mAP values compared to bbox detection.

References

[1]
M. A. Feroz, M. Sultana, M. R. Hasan, A. Sarker, P. Chakraborty, and T. Choudhury, “Object Detection and Classification from a Real-Time Video Using SSD and YOLO Models,” in Computational Intelligence in Pattern Recognition, 2022, pp. 37–47.
[2]
S. Saikia, E. Fidalgo, E. Alegre, and L. Fernández-Robles, “Object Detection for Crime Scene Evidence Analysis Using Deep Learning,” in Image Analysis and Processing - ICIAP 2017, 2017, pp. 14–24.
[3]
U. V. Navalgund and P. K., “Crime Intention Detection System Using Deep Learning,” in 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), 2018, pp. 1–6.
[4]
M. Boukabous and M. Azizi, “Image and video-based crime prediction using object detection and deep learning,” Bulletin of Electrical Engineering and Informatics, vol. 12, pp. 1630–1638, 2023.
[5]
Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, “Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields.” 2017.
[6]
C. Mayershofer, A. Hammami, and J. Fottner, “Multi-Class Object Detection Using 2D Poses,” in 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 647–652.
[7]
Y. Wang, M. Li, H. Cai, W.-M. Chen, and S. Han, “Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation.” 2022.
[8]
D. Pavllo, C. Feichtenhofer, D. Grangier, and M. Auli, “3d human pose estimation in video with temporal convolutions and semi-supervised training,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 7753–7762.
[9]
T. Hassan and A. B. Hamza, “Spatio-temporal MLP-graph network for 3D human pose estimation.” 2023.
[10]
A. Bożko and L. Ambroziak, “Influence of Insufficient Dataset Augmentation on IoU and Detection Threshold in CNN Training for Object Detection on Aerial Images,” Sensors, vol. 22, no. 23, 2022.
[11]
S. Zhang, C. Wang, W. Dong, and B. Fan, “A Survey on Depth Ambiguity of 3D Human Pose Estimation,” Applied Sciences, vol. 12, no. 20, 2022.
[12]
L. Fu, J. Zhang, and K. Huang, “ORGM: Occlusion Relational Graphical Model for Human Pose Estimation,” IEEE Transactions on Image Processing, vol. 26, no. 2, pp. 927–941, 2017.
[13]
U. Rafi, J. Gall, and B. Leibe, “A Semantic Occlusion Model for Human Pose Estimation From a Single Depth Image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015.
[14]
C. Zheng, W. Wu, C. Chen, T. Yang, S. Zhu, J. Shen, N. Kehtarnavaz, and M. Shah, “Deep Learning-Based Human Pose Estimation: A Survey,” ACM Comput. Surv., Jun. 2023.
[15]
Z. Cai and N. Vasconcelos, “Cascade R-CNN: Delving into High Quality Object Detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6154–6162.
[16]
G. Jocher, A. Chaurasia, and J. Qiu, “Ultralytics YOLOv8.” 2023.
[17]
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “SSD: Single shot multibox detector,” in European conference on computer vision, 2016, pp. 21–37.
[18]
Ultralytics, “Ultralytics YOLOv8 Docs.” Ultralytics, 2023.
[19]
NIJ, “Overview of Trace Evidence.” National Institute of Justice, 2023.
[20]
T.-Y. Lin, “COCO Detect Evaluation.” COCO Consortium, 2023.
[21]
M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: A retrospective,” International journal of computer vision, vol. 111, no. 1, pp. 98–136, 2015.
[22]
S. University and P. University, “ImageNet.” 2020.
[23]
Google, “Open Images Dataset V6.” Google, 2023.
[24]
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The KITTI dataset,” International Journal of Robotics Research (IJRR), vol. 32, no. 11, pp. 1231–1237, 2013.
[25]
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition, 2012, pp. 3354–3361.
[26]
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2012, pp. 3354–3361.
[27]
J. Cartucho, R. Ventura, and M. Veloso, “Robust Object Recognition Through Symbiotic Deep Learning In Mobile Robots,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 2336–2341.
[28]
W. Sun, S. Zhu, X. Ju, and D. Wang, “Deep learning based pedestrian detection,” 2018, pp. 1007–1011.
[29]
C. Currier, “Protocol Buffers,” in Mobile Forensics – The File Format Handbook: Common File Formats and File Systems Used in Mobile Devices, C. Hummert and D. Pawlaszczyk, Eds. Cham: Springer International Publishing, 2022, pp. 223–260.
[30]
Q. Koziol, “HDF5,” in Encyclopedia of Parallel Computing, D. Padua, Ed. Boston, MA: Springer US, 2011, pp. 827–833.
[31]
N. S. Ketkar, “Introduction to PyTorch,” in Deep Learning with Python, 2021.
[32]
S. Ahmed, P. Bisht, R. Mula, and S. S. Dhavala, “A Deep Learning Framework for Interoperable Machine Learning,” in Proceedings of the First International Conference on AI-ML Systems, 2021.
[33]
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. B. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional Architecture for Fast Feature Embedding,” arXiv preprint arXiv:1408.5093, 2014.
[34]
scikit-learn developers, “scikit-learn: Machine Learning in Python,” 2023.
[35]
R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. da Silva, “A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit,” Electronics, vol. 10, no. 3, p. 279, 2021.
[36]
A. Ahmed, X. Parra, and J. Ortega-Garcia, “A new approach for automated forensic trace evidence detection in crime scenes,” Forensic Science International, vol. 321, p. 110711, 2021.
[37]
C. D. Team, “CVAT (Computer Vision Annotation Tool) Annotator.” 2023.
[38]
Google, “MediaPipe: Pose estimation.” .
[39]
Retkowsky, “Human pose estimation with YOLOv7.”
[40]
A. Stock, “Get 10 free Adobe Stock images.” Adobe, 2023.
[41]
ultralytics, “Tips for Best Training Results.” 2023.
[42]
Wikipedia, “Surgical positions.” wikipedia, 2023.

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  • (2024)A comprehensive review of gait analysis using deep learning approaches in criminal investigationPeerJ Computer Science10.7717/peerj-cs.245610(e2456)Online publication date: 22-Nov-2024

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        cover image ACM Other conferences
        ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
        October 2023
        589 pages
        ISBN:9798400707988
        DOI:10.1145/3633637
        Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 28 February 2024

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        Author Tags

        1. Crime Scene Investigation
        2. Forensic science
        3. Object Detection
        4. POSE Estimation
        5. YOLOv8

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        • Research-article
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        • Refereed limited

        Funding Sources

        • Thailand Science Research and Innovation (TSRI) and Royal Police Cadet Academy Thailand

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        ICCPR 2023

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        • (2024)A comprehensive review of gait analysis using deep learning approaches in criminal investigationPeerJ Computer Science10.7717/peerj-cs.245610(e2456)Online publication date: 22-Nov-2024

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