Volume 40 Issue 2
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(2): 22-29
CHEN Xinqiang, ZHENG Jinbiao, LING Jun, WANG Zichuang, WU Jianjun, YAN Ying. Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network[J]. Journal of Transport Information and Safety, 2022, 40(2): 22-29. doi: 10.3963/j.jssn.1674-4861.2022.02.003
Citation: CHEN Xinqiang, ZHENG Jinbiao, LING Jun, WANG Zichuang, WU Jianjun, YAN Ying. Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network[J]. Journal of Transport Information and Safety, 2022, 40(2): 22-29. doi: 10.3963/j.jssn.1674-4861.2022.02.003
shu

Detecting Abnormal Behaviors of Workers at Ship Working Fields via Asynchronous Interaction Aggregation Network

doi: 10.3963/j.jssn.1674-4861.2022.02.003
  • 1.

    Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China

  • 2.

    Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China

  • 3.

    School of Transportation Engineering, Chang'an University, Xi'an 710064, China

  • Received Date: 2021-10-25
    Available Online: 2022-05-18
    Abstract
  • Identify abnormal behaviors of workers at ship working fields provides important information for intelligent shipping management and decision-making, which is conducive to promoting the development of smart ports and intelligent ships. To achieve this, an abnormal behavior recognition framework is proposed based on a novel asynchronous interaction aggregation (AIA) model. The proposed model implements the convolution operation on the maritime surveillance videos by using the YOLO algorithm. The convolution results are optimized using the feature pyramid to locate the human in each image. A method of joint learning of detection and an embedding model are then integrated to extract the spatial-temporal features of workers and objects. Furthermore, the proposed AIA model utilizes an interaction aggregation module that update multi-dimensional feature information in the feature pool to detect abnormal behaviors of workers at ship working fields. The results show that the average recognition accuracy of the proposed method is 91%, and the recognition accuracy is 85% at the working fields. For the ship bridge monitoring, the recognition accuracy of unsafe behaviors of crews can reach up to 97%. Based on its validity and reliability, the proposed framework can achieve good accuracy in a variety of ship working fields.

     

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    • References(19)
  • [1]
    张永锋, 龚建伟, 殷明. 新冠肺炎疫情对中国港航业的影响及其对策[J]. 交通运输工程学报, 2020, 20(3): 159-167. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202003019.htm

    ZHANG Y F, GONG J W, YIN M. Influences and response measures of COVID-19 epidemic on shipping and port industry in china[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 159-167. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202003019.htm
    [2]
    周翔宇, 吴兆麟, 王凤武, 等. 自主船舶的定义及其自主水平的界定[J]. 交通运输工程学报, 2019, 19(6): 149-162. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201906016.htm

    ZHOU X Y, WU Z L, WANG F W, et al. Definition of autonomous ship and its autonomy level[J]. Journal of Traffic and Transportation Engineering, 2019, 19(6): 149-162. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC201906016.htm
    [3]
    郑松, 吴晓林, 王飞跃, 等. 平行系统方法在自动化集装箱码头中的应用研究[J]. 自动化学报, 2019, 45(3): 490-504. https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201903005.htm

    ZHENG S, WU X L, WANG F Y, et al. Applying the parallel systems approach to automatic container terminal[J]. Acta Automatica Sinica, 2019, 45(3): 490-504(. in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-MOTO201903005.htm
    [4]
    ANDREA Z, JACOPO C, RICCARDO V, et al. Predicting intentions from motion: the subject-adversarial adaptation approach[J]. International Journal of Computer Vision, 2020, 128(1): 220-239. doi: 10.1007/s11263-019-01234-9
    [5]
    WANG H, KLÄSER A, SCHMID C, et al. Dense trajectories and motion boundary descriptors for action recognition[J]. International Journal of Computer Vision, 2013, 103 (1): 60-79. doi: 10.1007/s11263-012-0594-8
    [6]
    秦宇龙, 王永雄, 胡川飞, 等. 结合注意力与多尺度时空信息的行为识别算法[J]. 小型微型计算机系统, 2021, 42(9): 1802-1809. doi: 10.3969/j.issn.1000-1220.2021.09.002

    QIN Y L, WANG Y X, HU C F, et al. Action recognition algorithm based on attention and multiscale channels separation spatiotemporal information[J]. Journal of Chinese Computer Systems, 2021, 42(9): 1802-1809. (in Chinese) doi: 10.3969/j.issn.1000-1220.2021.09.002
    [7]
    YANG L H, LIU J, WANG Y M, et al. Online updating extended belief rule-based system for sensor-based activity recognition[J]. Expert Systems with Applications, 2021(186): 1-14.
    [8]
    谭等泰, 李世超, 常文文, 等. 多特征融合的行为识别模型[J]. 中国图像图形学报, 2020, 25(12): 2541-2552. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202012007.htm

    TAN D T, LI S C, CHANG W W, et al. Multi-feature fusion behavior recognition model[J]. Journal of Image and Graphics, 2020, 25(12): 2541-2552. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGTB202012007.htm
    [9]
    ALWANDO E H P, CHEN Y T, FANG W H. CNN-Based multiple path search for action tube detection in videos[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(1): 104-116. doi: 10.1109/TCSVT.2018.2887283
    [10]
    SHU X B, ZHANG L Y, SUN Y L, et al. Host-Parasite: graph LSTM-in-LSTM for group activity recognition[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(2): 663-674. doi: 10.1109/TNNLS.2020.2978942
    [11]
    WANG P C, LI W Q, GAO Z M, et al. Action recognition from depth maps using deep convolutional neural networks[J]. IEEE Transactions on Human-Machine Systems, 2016, 46(4): 498-509. doi: 10.1109/THMS.2015.2504550
    [12]
    陈影玉, 杨神化, 索永峰. 船舶行为异常检测研究进展[J]. 交通信息与安全, 2020, 38(5): 1-11. doi: 10.3963/j.jssn.1674-4861.2020.05.001

    CHEN Y Y, YANG S H, SUO Y F. Research progress of ship behavior anomaly detection[J]. Journal of Transport Information and Safety, 2020, 38(5): 1-11(. in Chinese) doi: 10.3963/j.jssn.1674-4861.2020.05.001
    [13]
    CHEN X Q, QI L, YANG Y S, et al. Video-based detection infrastructure enhancement for automated ship recognition and behavior analysis[J]. Journal of Advanced Transportation, 2020(2020): 1-12.
    [14]
    WEI Z K, XIE X L, ZHANG X J. AIS trajectory simplification algorithm considering ship behaviours[J]. Ocean Engineering, 2020(216): 1-10.
    [15]
    XUE J, CHEN Z J, PAPADIMITRIOU E, et al. Influence of environmental factors on human-like decision-making for intelligent ship[J]. Ocean Engineering, 2019(186): 1-14.
    [16]
    TANG J J, XIA J, MU X Z, et al. Asynchronous interaction aggregation for action detection[C]. European Conference on Computer Vision-ECCV 2020, Lecture Notes in Computer Science, Glasgow, UK: Springer, 2020.
    [17]
    CIPOLLA R, GAL Y, KENDALL A. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics[C]. 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA: IEEE, 2018.
    [18]
    GU C H, SUN C, ROSS D A, et al. AVA: A video dataset of spatio-temporally localized atomic visual actions[C]. 2018 IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA: IEEE, 2018.
    [19]
    FEICHTENHOFER C, FAN H Q, MALIK J, et al. SlowFast networks for video recognition[C]. 2019 International Conference on Computer Vision (ICCV), Seoul, Korea: IEEE, 2019.
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