Real-time Reconstruction and Simulation of Police Dog Posture Based on Multi-sensor Data Vest

doi:10.16182/j.issn1004731x.joss.201812031

Journal of System Simulation ›› 2018, Vol. 30 ›› Issue (12): 4738-4746.doi: 10.16182/j.issn1004731x.joss.201812031

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Real-time Reconstruction and Simulation of Police Dog Posture Based on Multi-sensor Data Vest

Zhu Chaozheng¹, He Ming^1,2,*, Yang Sheng^3,4, Yang Cheng¹, Song Zhenhua⁵

1. College of Command Control Engineer, Army Engineering University, Nanjing 211117, China;
2. Institute of Network Information, Academy of Systems Engineering, Academy of Military Sciences, Beijing 100071, China;
3. College of Information and Computer, HoHai University, Nanjing 211110, China;
4. Nanjing Huiying Electronics Technology Corporation, Nanjing 211100, China;
5. Nanjing Police Dog Research Institute, Ministry of Public Security, Nanjing 210012, China

Received:2018-04-10 Revised:2018-05-23 Online:2018-12-10 Published:2019-01-03

Abstract

Abstract: Real-time police dog posture reconstruction provides an intelligent, convenient and over-the-horizon interactive way for police on duty with dogs. For the biological movement characteristics of police dogs, a new multi-sensor dog data vest was designed and implemented. Based on multiple small size and low power consumption sensors, a new police dog motion fusion method was proposed to capture the different posture data of police dogs, including standing, sitting and prone position, etc. For frequent jitter of police dogs, this paper proposed a method of dog posture reconstruction based on multi-sensor data vest, and introduced an improved kernel function ELM algorithm to solve the optimal reconstruction posture results. The analysis and comparison of experimental simulation show that this method can reconstruct the real and credible police dog posture in real time.

Key words: posture reconstruction, motion capture, police dog, the ELM algorithm based on kernel function

CLC Number:

TP391.9

Zhu Chaozheng, He Ming, Yang Sheng, Yang Cheng, Song Zhenhua. Real-time Reconstruction and Simulation of Police Dog Posture Based on Multi-sensor Data Vest[J]. Journal of System Simulation, 2018, 30(12): 4738-4746.

References

[1] 孙勇, 宋天童. 大型安保勤务中警犬安检技术团队组建模式探析[J]. 中国人民公安大学学报(自然科学版). 2017, 23(1): 55-59.
Sun Yong, Song Tiantong.Analysis of the formation model of police dog inspection technical team in large-scale security service[J]. Journal of People's Public Security University of China (Sicience and Technology), 2017, 23(1): 55-59
[2] Cao Z, Simon T, Wei S E, et al.Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields[J]//IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2016: 1302-1310.
[3] Simon T, Joo H, Matthews I, et al.Hand Keypoint Detection in Single Images using Multiview Bootstrapping[J]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4645-4653.
[4] Wei S E, Ramakrishna V, Kanade T, et al.Convolutional Pose Machines[C]// Computer Vision and Pattern Recognition, 2016: 33-47.
[5] Chen X, Yuille A L.Articulated pose estimation by a graphical model with image dependent pairwise relations[C]//Advances in neural information processing systems. 2014: 1736-1744.
[6] Insafutdinov E, Pishchulin L, Andres B, et al.DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model[C]//. European Conference on Computer Vision, 2016: 34-50.
[7] Othmezouri G, Sakata I, Schiele B, et al.Monocular 3D pose estimation and tracking by detection[Z]. IEEE, 2015: 623-630.
[8] Fang B, Sun F, Liu H, et al.3D human gesture capturing and recognition by the IMMU-based data glove[J]. Neurocomputing, 2018, 277: 198-207.
[9] Xu Y, Wang Y, Su Y, et al.Research on the Calibration Method of Micro Inertial Measurement Unit for Engineering Application[J]. Journal of Sensors, 2016(1): 1-11.
[10] Lambrecht J M, Kirsch R F.Miniature low-power inertial sensors: promising technology for implantable motion capture systems[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014, 22(6): 1138-1147.
[11] Ahmed H, Tahir M.Improving the Accuracy of Human Body Orientation Estimation With Wearable IMU Sensors[J]. IEEE Transactions on Instrumentation & Measurement , 2017(99): 1-8.
[12] Maeda Y, Terao K, Shimokawa F, et al.Reduction of contact force dependence on the MEMS hardness sensor using reference plane to detect human body hardness[C]// Sensors. IEEE, 2016: 1-4.
[13] Dranitsyna E V.IMU calibration using sins navigation solution: Selection of the rate table motion scenario[C]// Saint Petersburg International Conference on Integrated Navigation Systems, 2017: 1-5.
[14] Tompson J, Goroshin R, Jain A, et al.Efficient object localization using convolutional networks[C] //Proce edings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 648-656.
[15] Belagiannis V, Zisserman A, editors. Recurrent Human Pose Estimation[C]// IEEE International Conference on Automatic Face & Gesture Recognition, 2017: 468-475.
[16] Lecun Y, Bengio Y, Hinton G.Deep learning[J]. Nature, 2015, 521(7553): 436.
[17] Abadi M, Agarwal A, Barham P, et al.TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems[J]. 2016.
[18] Ivanov A V, Zhilenkov A A.The use of IMU MEMS-sensors for designing of motion capture system for control of robotic objects[C]// IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering. IEEE, 2018: 890-893.
[19] Paden B E.Lower Bounds on the Frequency Estimation Error in Magnetically Coupled MEMS Resonant Sensors[J]. IEEE Transactions on Biomedical Circuits & Systems, 2016, 10(1): 18-24.

Real-time Reconstruction and Simulation of Police Dog Posture Based on Multi-sensor Data Vest

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