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Group behavior recognition based on deep hierarchical network

  • S.I. : ATCI 2019
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In order to achieve accurate judgment and identification of group behaviors, the hierarchical deep network model is constructed to judge the group behaviors. Through the construction of the hierarchical deep network model, the group behaviors are judged; the stability, accuracy, expression movement, orientation, error, and work efficiency of the model, as well as the support vector machine model and the convolution network model, are compared and analyzed. The hierarchical depth network model has distinct advantages in comparison with the support vector machine model and the convolution network model. The standard deviation of the hierarchical depth network model is 0.013, while the standard deviations of the other two models are larger than the hierarchical depth network model. The proposed algorithm has an excellent performance in detection accuracy and error. Compared with the other two models, it has certain advantages. In addition, the hierarchical depth network is used for recognizing human behaviors and orientations, as well as extracting and recovering the expressions in group behaviors. Compared with the other two models, the proposed model is also more efficient. The model used in this study has little influence on group behavior recognition by the regional environment and other factors, and there is no significant difference in the judgment results. The operation of the model is studied by identifying the group behaviors based on the hierarchical depth network. The research results show that the model proposed in this study has a comprehensive and excellent result, which also indicates that group behavior recognition is an overall result. It is necessary to have an accurate identification of multiple layer parameters. The research in this study has greatly improved the understanding of hierarchical deep network and group behavior recognition.

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References

  1. Lee J, Jin L, Park D et al (2016) Automatic recognition of aggressive behavior in pigs using a kinect depth sensor. Sensors 16(5):631

    Article  Google Scholar 

  2. Barros P, Parisi GI, Weber C et al (2017) Emotion-modulated attention improves expression recognition: a deep learning model. Neurocomputing 253:104–114

    Article  Google Scholar 

  3. Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  4. Kubilius J, Bracci S, de Beeck HPO (2016) Deep neural networks as a computational model for human shape sensitivity. PLoS Comput Biol 12(4):e1004896

    Article  Google Scholar 

  5. Ordóñez F, Roggen D (2016) Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition. Sensors 16(1):115

    Article  Google Scholar 

  6. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869

    Google Scholar 

  7. Kim BK, Roh J, Dong SY et al (2016) Hierarchical committee of deep convolutional neural networks for robust facial expression recognition. J Multimodal User Interfaces 10(2):173–189

    Article  Google Scholar 

  8. Cha YJ, Choi W, Büyüköztürk O (2017) Deep learning-based crack damage detection using convolutional neural networks. Comput Aided Civ Infrastruct Eng 32(5):361–378

    Article  Google Scholar 

  9. Mabrouk AB, Zagrouba E (2018) Abnormal behavior recognition for intelligent video surveillance systems: a review. Expert Syst Appl 91:480–491

    Article  Google Scholar 

  10. Han F, Reily B, Hoff W et al (2017) Space-time representation of people based on 3D skeletal data: a review. Comput Vis Image Underst 158:85–105

    Article  Google Scholar 

  11. Liu HL, Taniguchi T, Tanaka Y et al (2017) Visualization of driving behavior based on hidden feature extraction by using deep learning. IEEE Trans Intell Transp Syst 18(9):2477–2489

    Article  Google Scholar 

  12. Hassan MM, Uddin MZ, Mohamed A et al (2018) A robust human activity recognition system using smartphone sensors and deep learning. Future Gener Comput Syst 81:307–313

    Article  Google Scholar 

  13. Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248

    Article  Google Scholar 

  14. Sargano A, Angelov P, Habib Z (2017) A comprehensive review on handcrafted and learning-based action representation approaches for human activity recognition. Appl Sci 7(1):110

    Article  Google Scholar 

  15. Prakash C, Kumar R, Mittal N (2018) Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif Intell Rev 49(1):1–40

    Article  Google Scholar 

  16. Ranasinghe S, Al Machot F, Mayr HC (2016) A review on applications of activity recognition systems with regard to performance and evaluation. Int J Distrib Sens Netw. https://doi.org/10.1177/1550147716665520

    Article  Google Scholar 

  17. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83:187–205

    Article  Google Scholar 

  18. Nunez JC, Cabido R, Pantrigo JJ et al (2018) Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recognit 76:80–94

    Article  Google Scholar 

  19. Kahou SE, Bouthillier X, Lamblin P et al (2016) Emonets: multimodal deep learning approaches for emotion recognition in video. J Multimodal User Interfaces 10(2):99–111

    Article  Google Scholar 

  20. Romei V, Thut G, Silvanto J (2016) Information-based approaches of noninvasive transcranial brain stimulation. Trends Neurosci 39(11):782–795

    Article  Google Scholar 

  21. Prieto A, Prieto B, Ortigosa EM et al (2016) Neural networks: an overview of early research, current frameworks and new challenges. Neurocomputing 214:242–268

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of Shandong Province (ZR2018BF005), Project of Art Science in Shandong Province (201806506).

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Authors and Affiliations

  1. Shandong Provincial Research Center of Demonstration Engineering Technology for Urban and Rural Landscape, Taian, China

    Shuhan Qiao

  2. Department of Information Engineering, Shandong University of Science and Technology, Taian, China

    Lukun Wang

  3. College of Information Science and Engineering, Shandong Agricultural University, Taian, China

    Zhiyong Gao

  4. Forestry College, Shandong Agricultural University, Taian, China

    Shuhan Qiao

Authors
  1. Shuhan Qiao
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  2. Lukun Wang
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  3. Zhiyong Gao
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Correspondence to Lukun Wang.

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Cite this article

Qiao, S., Wang, L. & Gao, Z. Group behavior recognition based on deep hierarchical network. Neural Comput & Applic 32, 5389–5398 (2020). https://doi.org/10.1007/s00521-019-04699-4

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  • DOI: https://doi.org/10.1007/s00521-019-04699-4

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