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The Understanding of Traffic Police Intention Based on Visual Awareness

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Abstract

This paper presents a system that can be used to automatically recognize the intention of traffic police based on visual awareness, which is important for driver assistance systems and autonomous vehicles. Traffic police play an important role in traffic scenes because the presence of traffic police often means there are traffic jams, accident-prone areas, or traffic failures. In this system, key points of the human body used to express the spatial pose of traffic police are extracted by OpenPose, and these key points are used to generate a spatio-temporal map by motion representation. Then, the graph convolutional network and modified Transformer are respectively used to obtain the spatial features and temporal features from the spatio-temporal map. Finally, the above features are used to infer the intention of traffic police in continuous frame images. Experimental results demonstrate that the proposed method had a higher accuracy than other state-of-the-art recognition algorithms in understanding traffic police intention.

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References

  1. He J, Zhang C, He X, Dong R (2020) Visual recognition of traffic police gestures with convolutional pose machine and handcrafted features. Neurocomputing 390:248–259

    Article  Google Scholar 

  2. Zafri NM, Rony AI, Adri N (2020) Study on pedestrian compliance behavior at vehicular traffic signals and traffic-police-controlled intersections. In J Intell Transport Syst Res 18(3):400–411

    Google Scholar 

  3. Cai Z, Guo F (2015) Max-covering scheme for gesture recognition of Chinese traffic Police. Pattern Anal 18(2):403–418

    Article  MathSciNet  Google Scholar 

  4. Guo F, Tang J, Cai Z (2013) Automatic recognition of Chinese traffic police gesture based on max-covering scheme. Adv Inf Sci Serv 5(1):403–418

    Google Scholar 

  5. Yuan T, Wang B (2010) Accelerometer-based Chinese traffic police gesture recognition system. Chin J Electron 19(2):270–274

    MathSciNet  Google Scholar 

  6. Hong Chaoqun Yu, Jun ZJ et al (2019) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Industr Inf 15(7):3952–3961

    Article  Google Scholar 

  7. Dara S, Tumma P (2018) Feature extraction by using deep learning: a survey. 2018 Second Int Conf Electron Commun Aerosp Technol 2018:1795–1801

    Article  Google Scholar 

  8. Sridhar R, Natalie D (2018) Customer perception analysis using deep learning and NLP. Procedia Comput Sci 140:170–178

    Article  Google Scholar 

  9. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–445

    Article  Google Scholar 

  10. Quan-shi Z, Song-chun Z (2018) Visual interpretability for deep learning: a survey. Front Inf Technol Electron Eng 19(1):27–39

    Article  Google Scholar 

  11. Jiuxiang Gu, Zhenhua W, Jason K et al (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377

    Article  Google Scholar 

  12. Mahmoud A-A, Aya N, Kholoud A (2018) Deep learning for Arabic NLP: a survey. Journal of computational science 26:522–531

    Article  Google Scholar 

  13. Alameh M, Abbass Y, Ibrahim A et al (2021) Touch modality classification using recurrent neural networks. IEEE Sens J 21(8):9983–9993

    Article  Google Scholar 

  14. Zhe C, Gines H, Tomas S (2021) OpenPose: Realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43:172–186

    Article  Google Scholar 

  15. Fang HS, Xie S, Tai YW, Lu C (2017) RMPE: regional multi-person pose estimation. Proc IEEE Int Conf Comput Vis 2017:2334–2343

    Google Scholar 

  16. Caetano C, Brémond F, Schwartz WR (2019) Skeleton Image Representation for 3D Action Recognition based on Tree Structure and Reference Joints. 32nd IBGRAPI Conf Graph Patterns Imag, SIBRAPI 2019:16–23

    Google Scholar 

  17. Tripathi G, Singh K, Vishwakarma DK (2019) Convolutional neural networks for crowd behaviour analysis: a survey. Vis Comput 35(5):753–776

    Article  Google Scholar 

  18. Zhang J, Cao Y, Wu Q (2021) Vector of locally and adaptively aggregated descriptors for image feature representation. Pattern Recogn 116:107952. https://doi.org/10.1016/j.patcog.2021.107952

    Article  Google Scholar 

  19. Jun Yu, Dacheng T, Meng W et al (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 4(45):767–779

    Google Scholar 

  20. Jun Yu, Min T, Hongyuan Z (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2019.2932058

    Article  Google Scholar 

  21. Zhou J, Cui G, Hu S, Zhang Z et al (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81

    Article  Google Scholar 

  22. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32:4–24

    Article  MathSciNet  Google Scholar 

  23. Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: a survey. IEEE Trans Knowl Data Eng 34:249–270

    Article  Google Scholar 

  24. Varghese AS, Sarang S, Yadav V et al (2020) Bidirectional LSTM joint model for intent classification and named entity recognition in natural language understanding. Adv Int Syst Comput 941:58–268

    Google Scholar 

  25. Shua G, Yuefei H, Shuo Z et al (2020) Short-term runoff prediction with GRU and LSTM networks without requiring time step optimization during sample generation. J Hydrol 589:1–11

    Google Scholar 

  26. Shu X, Zhang L, Sun Y, Tang J (2020) Host–parasite: graph LSTM-in-LSTM for group activity recognition. IEEE Trans Neural Netw Learn Syst 32(2):663–674

    Article  Google Scholar 

  27. Tan HX, Aung NN, Tian J, Chua MCH, Yang YO (2019) Time series classification using a modified LSTM approach from accelerometer-based data: a comparative study for gait cycle detection. Gait Posture 74:128–134

    Article  Google Scholar 

  28. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 2017:5998–6008

    Google Scholar 

  29. Lee JB, Rossi RA, Kim S, Ahmed NK, Koh E (2019) Attention models in graphs: a survey. ACM Trans Knowl Discov Data 11(6):1–25

    Article  Google Scholar 

  30. Fu J, Zheng H, Mei T et al. Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition. In: CVPR2017

  31. Wang F, Jiang M, Qian C et al. Residual attention network for image classification. In CVPR2017

  32. Chen LC, Yang Y, Wang J, et al. (2016) Attention to scale: Scale-aware semantic image segmentation. In: CVPR2016

  33. Wang S, Li BZ, Khabsa M, et al. (2020) Self-attention with linear complexity. In: FAIR NIPS 2020

  34. Zhou J, Cui G, Zhang Z et al. (2018) Graph neural networks: a review of methods and applications. arXiv:https://arxiv.org/abs/1812.08434

  35. Tetk IV, Karpov P, Van Deursen R, Godin G (2020) State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis. Nat Commun 11(1):1–11

    Article  Google Scholar 

  36. Han K, Wang Y, Chen H et al. A survey on visual transformer. arXiv:https://arxiv.org/abs/2101.01169

  37. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. Proc IEEE Conf Comput Vis Pattern Recogn 2016:770–778

    Google Scholar 

  38. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: CVPR2017

  39. Ji S, Xu W, Yang M, Yu K (2013) 3d convolutional neural networks for human action recognition. IEEE Trans Pattern Anal 35(1):221–231

    Article  Google Scholar 

  40. Xingjian S, Chen Z, Wang H, Yeung DY, Wong WK, Woo WC (2015) Convolutional lstm network: a machine learning approach for precipitation nowcasting. Adv Neural Inf Process Syst 2015:802–810

    Google Scholar 

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Funding

This work was supported by the National Natural Science Foundation of China (52172382, 61976039) and the China Fundamental Research Funds for the Central Universities (DUT20GJ207), and Science and Technology Innovation Fund of Dalian (2021JJ12GX015).

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Authors and Affiliations

  1. Faculty of Vehicle Engineering and Mechanics, School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China

    Jing Lian, Zhenghao Wang, Linhui Li & Yafu zhou

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  1. Jing Lian
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  2. Zhenghao Wang
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  3. Linhui Li
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  4. Yafu zhou
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Correspondence to Linhui Li.

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Lian, J., Wang, Z., Li, L. et al. The Understanding of Traffic Police Intention Based on Visual Awareness. Neural Process Lett 54, 2843–2859 (2022). https://doi.org/10.1007/s11063-022-10741-9

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  • DOI: https://doi.org/10.1007/s11063-022-10741-9

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