Abstract
In order to identify and respond appropriately to suspicious behaviour in public locations, it can be important to understand the typical behaviours in a particular setting. An observation study has been carried out at two libraries to explore the entry behaviours as people approach card access barriers in these locations. Seven hours of video data have been collected over four observation periods. The recordings have been analysed by a researcher, using a behavioural framework that has been developed in previous research in a related study context. This framework was used as a prompt to look in more detail at a range of different aspects of behaviours that may be observable. The findings, including draft requirements for smart camera technologies to support security staff, were discussed in an interview with a security expert. The study identified common types of behaviour, with some differences evident when accessing the libraries alone or as part of a group. A number of interesting aspects of behaviour were observed on approach to, or at the point of interacting with, the access barrier for the buildings. These included observation of several behavioural cues that could be early indicators of anomalous behaviours that were in progress. The study has demonstrated the feasibility of adapting and using the behavioural framework to explore behaviours in new settings.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vats E, Chan CS (2016) Early detection of human actions—a hybrid approach. Appl Soft Comput 46:953–966
Chen Z, Tian Y, Zeng W, Huang T (2015) Detecting abnormal behaviors in surveillance videos based on fuzzy clustering and multiple auto-encoders. In: IEEE international conference on multimedia and expo (ICME), pp 1–6
Popoola OP, Wang K (2012) Video-based abnormal human behavior recognition—a review. IEEE Trans Syst Man Cybern Part C (Appl Rev) 42(6):865–878
Tehrani MA, Kleihorst R, Meijer P, Spaanenburg L (2009) Abnormal motion detection in a real-time smart camera system. In: Third ACM/IEEE international conference on distributed smart cameras, ICDSC. IEEE, pp 1–7
Shao Z, Cai J, Wang Z (2017) Smart monitoring cameras driven intelligent processing to big surveillance video data. IEEE Trans Big Data 99:1–13
Ozer B, Wolf M (2014) A train station surveillance system: challenges and solutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 638–643
Wolf W, Ozer B, Lv T (2002) Smart cameras as embedded systems. Computer 35(9):48–53
Kawamura A, Yoshimitsu Y, Kajitani K, Naito T, Fujimura K, Kamijo S (2011) Smart camera network system for use in railway stations. In: IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 85–90
Rinner B, Wolf W (2008) An introduction to distributed smart cameras. Proc IEEE 96(10):1565–1575
Rinner B, Wolf W (2008) A bright future for distributed smart cameras. Proc IEEE 96(10):1562–1564
Hampapur A, Brown L, Connell J, Pankanti S, Senior A, Tian Y (2003) Smart surveillance: applications, technologies and implications. In: Proceedings of the joint fourth international conference on information, communications and signal processing, and fourth pacific rim conference on multimedia, vol 2. IEEE, pp 1133–1138
Ryan B (2018) Developing a framework of behaviours before suicides at railway locations. Ergonomics 61(5):605–626
Krahnstoever N, Tu P, Yu T, Patwardhan K, Hamilton D, Yu B, Greco C, Doretto G (2009) Intelligent video for protecting crowded sports venues. In: Sixth IEEE international conference on advanced video and signal based surveillance, AVSS 2009. IEEE, pp 116–121
Valera M, Velastin SA (2005) Intelligent distributed surveillance systems: a review. IEEE Proc Vis Image Signal Process 152(2):192–204
Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. J Pers Soc Psychol 17(2):124
Aviezer H, Trope Y, Todorov A (2010) 2) Body cues, not facial expressions, discriminate between intense positive and negative emotions. Science 338:1225–1229
Martinez L, Falvello VB, Aviezer H, Todorov A (2016) Contributions of facial expressions and body language to the rapid perception of dynamic emotions. Cogn Emotion 30(5):939–952
Arroyo R, Yebes JJ, Bergasa LM, Daza IG, Almazán J (2015) Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls. Expert Syst Appl 42(21):7991–8005
Fujimura K, Yoshimitsu Y, Naito T, Kamijo S (2010) Behavior understanding at railway station by postures and the pseud-trellis analysis of trajectories. In: 13th international IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 1116–1122
Vrij A, Granhag PA, Porter S (2010) Pitfalls and opportunities in nonverbal and verbal lie detection. Psychol Sci Public Interest 11(3):89–121
Schmidt RA (1982) Motor control and learning: a behavioural emphasis. Human Kinetics, Champaign
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ryan, B., Vijayan, A. (2019). Classifying Normal and Suspicious Behaviours When Accessing Public Locations. In: Bagnara, S., Tartaglia, R., Albolino, S., Alexander, T., Fujita, Y. (eds) Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018). IEA 2018. Advances in Intelligent Systems and Computing, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-96074-6_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-96074-6_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96073-9
Online ISBN: 978-3-319-96074-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)