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Life detection and non-contact respiratory rate measurement in cluttered environments

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Abstract

A method is proposed in this paper for life detection and non-contact respiratory rate measurement in cluttered environments. Only an RGB video of the detection area is required. In the method, spatial filtering is firstly applied to each frame of the video for image denoising. Gray level compensation follows to compensate for the change of gray level caused by the environment light. Thirdly, the gray levels of each pixel over time are filtered separately by a low-pass filter. At last, the human is located and the respiratory rate is measured. Tests on a self-made dataset show that an accuracy of 76.7% is achieved by the proposed method, which is better than that of the Convolutional Neural Networks (30%) and the histogram of oriented gradients (3.3%).

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References

  1. Aoki H, Miyazaki M, Nakamura H, Furukawa R, Sagawa R, Kawasaki H (2012) Non-contact respiration measurement using structured light 3-D sensor. In: Proceedings of the SICE annual conference, pp 614–618

  2. Awad F, Shamroukh R (2014) Human detection by robotic urban search and rescue using image processing and neural networks. Int J Intell Sci 4:39–53. https://doi.org/10.4236/ijis.2014.42006

    Article  Google Scholar 

  3. Bai Z, Gao Q, Yu X (2019) Moving object detection based on adaptive loci frame difference method. In: 2019 IEEE international conference on mechatronics and automation (ICMA), pp 2218–2223. https://doi.org/10.1109/ICMA.2019.8816624

  4. Burba N, Bolas M, Krum DM, Suma EA (2012) Unobtrusive measurement of subtle nonverbal behaviors with the Microsoft Kinect. In: Proceedings of the IEEE virtual reality workshops (VR), pp 1–4. https://doi.org/10.1109/VR.2012.6180952

  5. Cao Z, Simon T, Wei SE, Sheikh Y (2017) Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7291–7299

  6. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: CVPR, pp 886–893. https://doi.org/10.1109/CVPR.2005.177

  7. Deng J, Dong W, Socher R, Li LJ, Li K, Li FF (2009) Imagenet: a large-scale hierarchical image database. In: CVPR, pp 248–255. https://doi.org/10.1109/CVPR.2009.5206848

  8. Donelli M (2011) A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm. Prog Electromagn Res 19:173–181. https://doi.org/10.2528/PIERM11061206

    Article  Google Scholar 

  9. Doulamis N, Agrafiotis P, Athanasiou G, Amditis A (2017) Human object detection using very low resolution thermal cameras for urban search and rescue. In: Proceedings 10th international conference pervasive technologies related to assistive environments, pp 311–318. https://doi.org/10.1145/3056540.3076201

  10. Fang CY, Hsieh HH, Chen SW (2015) A vision-based infant respiratory frequency detection system. In: International conference on digital image computing: techniques and applications (DICTA), pp 1–8. https://doi.org/10.1109/DICTA.2015.7371224

  11. Fang Z, Yi W, Kong L (2016) A tracking approach for low observable target using plot-sequences of multi-frame detection. In: 19th International conference on information fusion (FUSION), pp 1427–1433

  12. García-Martín Ál, Martínez JM (2015) People detection in surveillance: classification and evaluation. IET Comput Vis 9(5):779–788

    Article  Google Scholar 

  13. Huang J , Rathod V (2017) Supercharge your computer vision models with the TensorFlow Object Detection API. Available: https://ai.googleblog.com/2017/06/supercharge-your-computer-vision-models.html. Accessed 10 Dec 2019

  14. Jiang Y, Ma J (2015) Combination features and models for human detection. In: CVPR, pp 240–248. https://doi.org/10.1109/CVPR.2015.7298620

  15. Kim Y, Moon T (2015) Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geosci Remote Sens Lett 13:8–12. https://doi.org/10.1109/LGRS.2015.2491329

    Article  Google Scholar 

  16. Kumdee O, Ritthipravat P (2015) Repetitive motion detection for human behavior understanding from video images. In: 2015 IEEE international symposium on signal processing and information technology (ISSPIT) Abu Dhabi, pp 484–489

  17. Lee T-S, Tsui BMW (2015) The development and initial evaluation of a realistic simulated SPECT dataset with simultaneous respiratory and cardiac motion for gated myocardial perfusion SPECT. Phys Med Biol 60(4):1399–1413

    Article  Google Scholar 

  18. Levin E, Zarnowski A, McCarty JL, Bialas J, Banaszek A (2016) Feasibility study of inexpensive thermal sensors and small UAS deployment for living human detection in rescue missions application scenarios. In: The international archives of the photogrammetry, remote sensing and spatial information sciences XLI-B8, pp 99–103. https://doi.org/10.5194/isprs-archives-XLI-B8-99-2016

  19. Lin TY, Maire M, Belongie S et al (2014) Microsoft coco: common objects in context. In: Proceedings of the 13th European conference on computer vision. https://doi.org/10.1007/978-3-319-10602-1_48

  20. Nguyen DT, Li W, Ogunbona PO (2016) Human detection from images and videos: a survey. Pattern Recognit 51:148–175. https://doi.org/10.1016/j.patcog.2015.08.027

    Article  Google Scholar 

  21. Rohrbach A et al (2015) A dataset for movie description. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3203–3212

  22. Sommer LW, Teutsch M, Schuchert T et al (2016) A survey on moving object detection for wide area motion imagery. In: 2016 IEEE Winter conference on applications of computer vision (WACV), pp 1–9. https://doi.org/10.1109/WACV.2016.7477573

  23. Sulistijono IA, Risnumawan A (2016) From concrete to abstract: multilayer neural networks for disaster victims detection. In: International electronics symposium, pp 93–98. https://doi.org/10.1109/ELECSYM.2016.7860982

  24. Tran QV, Su SF, Chuang CC et al (2017) Real-time non-contact breath detection from video using adaboost and Lucas-Kanade algorithm. In: Proceedings of the joint 17th world congress of international fuzzy systems association and 9th international conference on soft computing and intelligent systems (IFSA-SCIS), pp 1–4. https://doi.org/10.1109/IFSA-SCIS.2017.8023320

  25. Villarroel M, Guazzi A, et al. (2014) Continuous non-contact vital sign monitoring in neonatal intensive care unit. Healthc Technol 1:87–91. https://doi.org/10.1049/htl.2014.0077

    Article  Google Scholar 

  26. Wu C, Yang Z, Zhou Z, Liu X, Liu Y, Cao J (2015) Non-invasive detection of moving and stationary human with wifi. IEEE J Sel Areas Commun 33:2329–2342. https://doi.org/10.1109/JSAC.2015.2430294

    Article  Google Scholar 

  27. Zhang F, Zhang D, Xiong J, Wang H, Niu K, Jin B, Wang Y (2018) From fresnel diffraction model to fine-grained human respiration sensing with commodity Wi-Fi devices. Proc ACM Interact Mob Wearable Ubiquitous Technol 2:Article 53. https://doi.org/10.1145/3191785

    Google Scholar 

  28. Zhao M, Li T, Abu Alsheikh MA, Tian Y, Zhao H, Torrralba A, Katabi D (2018) Through-wall human pose estimation using radio signals. In: CVPR, pp 7356–7365. https://doi.org/10.1109/CVPR.2018.00768

  29. Zhou F, Torre FDL (2014) Spatio-temporal matching for human detection in video. In: European Conference on Computer Vision, vol 8694, pp 62–77. https://doi.org/10.1007/978-3-319-10599-4_5

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Acknowledgements

This work was supported in part by the Manned Aerospace Research Project of China [grant number 060601].

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  1. School of Mechanical Engineering, Science, Huazhong University of Science and Technology, Wuhan, 430074, China

    Shiqi Li, Haipeng Wang, Shuze Wang & Shuai Zhang

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  1. Shiqi Li
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  2. Haipeng Wang
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Correspondence to Haipeng Wang.

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Li, S., Wang, H., Wang, S. et al. Life detection and non-contact respiratory rate measurement in cluttered environments. Multimed Tools Appl 79, 32065–32077 (2020). https://doi.org/10.1007/s11042-020-09510-4

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