Skip to main content
Springer Nature Link
Log in

Hazard Control in Industrial Environments: A Knowledge-Vision-Based Approach

  • Conference paper
  • First Online:
Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017 (ISAT 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 657))

Abstract

This paper proposes the integration of image processing techniques (such as image segmentation, feature extraction and selection) and a knowledge representation approach in a framework for the development of an automatic system able to identify, in real time, unsafe activities in industrial environments. In this framework, the visual information (feature extraction) acquired from video-camera images and other context based gathered data are represented as Set of Experience Knowledge Structure (SOEKS), a formal decision event for reasoning and risk evaluation. Then, grouped sets of decisions from the same category are stored as decisional experience Decisional DNA (DDNA) to support future decision making events in similar input images. Unlike the existing sensor and vision-based approaches, that required rewriting most of the code when a condition, situation or requirement changes, our platform is an adaptable system capable of working in a variety of video analysis scenarios. Depending on the safety requirements of each industrial environment, users can feed the system with flexible rules and in the end, the platform provides decision makers with hazard evaluations that reuse experience for event identification and correction.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Safe Work Australia SWA: Disease and injury statistics. https://www.safeworkaustralia.gov.au/collection/australian-workers-compensation-statistics

  2. Safe Work Australia: Australian Work Health and Safety Strategy 2012–2022. Creative Commons (2012)

    Google Scholar 

  3. Safetycare Australia: Recognition, evaluation and control of hazards, Victoria (2015)

    Google Scholar 

  4. Han, S., Lee, S.: A vision-based motion capture and recognition framework for behavior based safety management. Autom. Constr. 35, 131–141 (2013)

    Article  Google Scholar 

  5. Little, S., Jargalsaikhan, I., Clawson, K., Li, H., Nieto, M., Direkoglu, C., et al.: An information retrieval approach to identifying infrequent events in surveillance video, Dallas (2013)

    Google Scholar 

  6. Zambrano, A., Toro, C., Nieto, M., Sotaquirá, R., Sanín, C., Szczerbicki, E.: Video semantic analysis framework based on run-time production rules – towards cognitive vision. J. Univ. Comput. Sci. 21(6), 856–870 (2015)

    MathSciNet  Google Scholar 

  7. Aggarwal, J.K., Ryoo, M.S.: Human activity analysis. ACM Comput. Surv. 43(3), 1–43 (2011)

    Article  Google Scholar 

  8. Mosberger, R., Andreasson, H., Lilienthal, A.J.: Multi-human tracking using high-visibility clothing for industrial safety. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 638–644. IEEE (2013)

    Google Scholar 

  9. Zin, T.T., Tin, P., Hama, H., Toriu, T.: An integrated framework for detecting suspicious behaviors in video surveillance. In: IS&T/SPIE Electronic Imaging, International Society for Optics and Photonics, p. 902614 (2014)

    Google Scholar 

  10. Favoreel, W.: Pedestrian sensing for increased traffic safety and efficiency at signalized intersections. In: 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (2011)

    Google Scholar 

  11. Mokhtari, G., Zhang, Q., Hargrave, C., Ralston, J.C.: Non-wearable UWB sensor for human identification in smart home. IEEE Sens. J. 17(11), 3332–3340 (2017)

    Article  Google Scholar 

  12. Yin, Y., Davis, R.: Real-time continuous gesture recognition for natural human-computer interaction. In: IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), pp. 113–120 (2014)

    Google Scholar 

  13. Gade, R., Jørgensen, A., Jensen, M.M., Alldieck, T., Abou-Zleikha, M., Christensen, M.G., Moeslund, T.B., Poulsen, M.K., Larsen, R.G., Franch, J., Jensen, M.M.: Automatic analysis of activities in sports arenas using thermal cameras. In: 12th International Conference on Signal-Image Technology & Internet-Based Systems. IEEE Computer Society Press (2016)

    Google Scholar 

  14. Moughlbay, A.A., Herrero, H., Pacheco, R., Outón, J.L., Sallé, D.: Reliable workspace monitoring in safe human-robot environment. In: International Joint Conference SOCO 2016-CISIS 2016-ICEUTE 2016, ICEUTE 2016, SOCO 2016, CISIS 2016. Advances in Intelligent Systems and Computing, vol. 527. Springer, Cham (2017)

    Google Scholar 

  15. Mukhopadhyay, S.C.: Wearable sensors for human activity monitoring: a review. IEEE Sens. J. 15(3), 1321–1330 (2015)

    Article  Google Scholar 

  16. Dohnálek, P., Gajdoš, P., Peterek, T.: Human activity recognition: classifier performance evaluation on multiple datasets. J. Vibroeng. 16(3) (2014)

    Google Scholar 

  17. Ponce, H., Miralles-Pechuán, L., Martínez-Villaseñor, M.D.L.: A flexible approach for human activity recognition using artificial hydrocarbon networks. Sens. Basel Sens. 16 (2016)

    Google Scholar 

  18. Palumbo, F., Gallicchio, C., Pucci, R., Micheli, A.: Human activity recognition using multisensor data fusion based on reservoir computing. J. Ambient Intell. Smart Environ. 8(2), 87–107 (2016)

    Article  Google Scholar 

  19. Wang, X., Zhang, B., Zhang, F., Teng, G., Sun, Z., Wei, J.: Toward robust activity recognition: hierarchical classifier based on Gaussian process. IDA 20(3), 701–717 (2016)

    Article  Google Scholar 

  20. Li, P., Meziane, R., Otis, M. J.-D., Ezzaidi, H., Cardou, P.: A smart safety helmet using IMU and EEG sensors for worker fatigue detection. In: Proceedings of the IEEE International Symposium on Robotic and Sensors Environments (ROSE), pp. 55–60. IEEE (2014)

    Google Scholar 

  21. Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from RGBD images. In: IEEE International Conference on Robotics and Automation, pp. 842–849. IEEE (2012)

    Google Scholar 

  22. Nunes, J.F., Moreira, P.M., Tavares, J.E.: Human motion analysis and simulation tools: a survey. In: Handbook of Research on Computational Simulation and Modeling in Engineering, pp. 359–388. IGI Global (2016)

    Google Scholar 

  23. Farooq, A., Jalal, A., Kamal, S.: Dense RGB-D map-based human tracking and activity recognition using skin joints features and self-organizing map. KSII TIIS, 9(5) (2015)

    Google Scholar 

  24. Roitberg, A., Perzylo, A., Somani, N., Giuliani, M., Rickert, M., Knoll, A.: Human activity recognition in the context of industrial human-robot interaction. In: Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1–10. IEEE (2014)

    Google Scholar 

  25. Xu, X., Tang, J., Zhang, X., Liu, X., Zhang, H., Qiu, Y.: Exploring techniques for vision based human activity recognition: methods, systems, and evaluation. Sens. Basel Sens. 13(2), 1635–1650 (2013)

    Article  Google Scholar 

  26. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. Proc. CVPR 1, 886–893 (2005)

    Google Scholar 

  27. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: speeded up robust features. In: Computer Vision–ECCV, vol. 3951, pp. 404–417 (2006)

    Google Scholar 

  28. Ojala, T., Pietikäinen, M., Harwood, D.: Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, pp. 582–585 (1994)

    Google Scholar 

  29. Viola, P., Jones, M.: Robust real-time object detection. IJCV 57(2), 137–154 (2004)

    Article  Google Scholar 

  30. Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)

    MATH  Google Scholar 

  31. Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  32. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  33. Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3, 185–205 (2005)

    Article  Google Scholar 

  34. Sanin, C., Szczerbicki, E.: Set of experience: a knowledge structure for formal decision events. Found. Control Manag. Sci. 3, 95–113 (2005)

    Google Scholar 

  35. Sanín, C., Szczerbicki, E.: Towards the construction of decisional DNA: a set of experience knowledge structure java class within an ontology system. Cybern. Syst. 38, 859–878 (2007)

    Article  MATH  Google Scholar 

  36. Qian, G., Sural, S., Gu, Y., Pramanik, S.: Similarity between Euclidean and cosine angle distance for nearest neighbor queries. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 1232–1237. ACM (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Newcastle, Newcastle, NSW, Australia

    Caterine Silva de Oliveira & Cesar Sanin

  2. Gdansk University of Technology, Gdansk, Poland

    Edward Szczerbicki

Authors
  1. Caterine Silva de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cesar Sanin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edward Szczerbicki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Caterine Silva de Oliveira .

Editor information

Editors and Affiliations

  1. Department of Management Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Zofia Wilimowska

  2. Department of Computer Science, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Leszek Borzemski

  3. Department of Computer Science, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Jerzy Świątek

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

de Oliveira, C.S., Sanin, C., Szczerbicki, E. (2018). Hazard Control in Industrial Environments: A Knowledge-Vision-Based Approach. In: Wilimowska, Z., Borzemski, L., Świątek, J. (eds) Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology – ISAT 2017. ISAT 2017. Advances in Intelligent Systems and Computing, vol 657. Springer, Cham. https://doi.org/10.1007/978-3-319-67223-6_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-67223-6_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67222-9

  • Online ISBN: 978-3-319-67223-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics