Security surveillance entails many cognitive challenges (e.g., task interruption, vigilance decrements, cognitive overload). To help surveillance operators overcome these difficulties and perform more efficient visual search, gaze-based intelligent systems can be developed. The present study aimed at testing the impact of the Scantracker system—which pinpointed neglected cameras while detecting and correcting attentional tunneling and vigilance decrease—on human scanning behavior and surveillance performance. Participants took part in a surveillance simulation, monitoring cameras and searching for ongoing incidents, and half of them was supported by the Scantracker. Although behavioral surveillance performance was not improved, participants supported by the Scantracker showed more efficient gaze-based measures of surveillance. Moreover, some of these measures were associated with performance, suggesting that scan pattern improvements might lead indirectly to more efficient incident detection. Overall, these results speak to the potential of using gaze- aware intelligent systems to support surveillance operators.
Get full access to this article
View all access options for this article.
References
1.
CaldwellJ. A.MallisM. M.CaldwellJ. L.PaulM. A.MillerJ. C.NeriD. F. (2009). Fatigue countermeasures in aviation. Aviation, Space, and Environmental Medicine, 80(1), 29–59.
2.
de BruijnD.JansenJ.LeniorD.PikaarR.SchreibersK. (2016). Human factor guidelines and workload in CCTV design. In de WaardD.BrookhuisK. A.ToffettiA.StuiverA.WeikertC.CoelhoD.… MeratN. (Eds.), Proceedings of the Human Factors and Ergonomics Society Europe Chapter 2015 Annual Conference (pp. 209–219).
3.
DehaisF.CausseM.TremblayS. (2011). Mitigation of conflicts with automation. Human Factors, 53(5), 448–460.
4.
DonaldF. M. (2019). Information processing challenges and research directions in CCTV surveillance. Cognition, Technology & Work, 21, 487–496.
5.
FoyS. (2010). CCTV in homicide investigations. Scotland Yard report, UK.
6.
GillM.LittleR.SpriggsA.AllenJ.ArgomanizJ.WaplesS. (2005). Assessing the impact of CCTV: The Hawkeye case study. Home Office Online Report. London, UK: Home Office.
7.
GrantD.WilliamsD. (2011). The importance of perceiving social contexts when predicting crime and antisocial behavior in CCTV images. Legal and Criminological Psychology, 16(2), 307–322.
8.
GreviG.HellyD.KeohaneD. (2009). European security and defence policy: The first 10 years (1999–2009). Paris, France: The European Union Institute for Security Studies.
9.
HartS. G.StavelandL. E. (1988). Development of NASA- TLX (Task Load Index): Results of empirical and theoretical research. Advances in Psychology, 52, 139–183.
10.
HockeyG. R. J. (2003). Operator functional state as a framework for the assessment of performance degradation. In GaillardA. W. K.HockeyG. R. J.BurovO. (Eds.), Operator functional state: The assessment and prediction of human performance degradation in complex tasks (pp. 8–23). Amsterdam, Netherlands: IOS Press.
11.
HodgettsH. M.ChamberlandC.Latulippe-ThériaultJ.-D.VachonF.TremblayS. (2018). Priority or parity? Scanning strategies and detection performance of novice operators in urban surveillance. Proceedings of the Human Factors and Ergonomics Society, 62, 1113–1117.
12.
HodgettsH. M.VachonF.ChamberlandC.TremblayS. (2017). See no evil: Cognitive challenges of security surveillance and monitoring. Journal of Applied Research in Memory and Cognition, 6(3), 230–243.
13.
HowardC. J.GilchristI. D.TrosciankoT.BeheraA.HoggD. C. (2011). Task relevance predicts gaze in videos of real moving scenes. Experimental Brain Research, 214(1), 131–137.
14.
ImbertJ.-P.HodgettsH. M.PariseR.VachonF.DehaisF.TremblayS. (2014). Attentional costs and failures in air traffic control notifications. Ergonomics, 57(12), 1817–1832.
15.
KruegleH. (2004). CCTV surveillance. In FennellyL. J. (Ed.), Effective physical security (pp. 209–217). Burlington, MA: Elsevier Inc.
16.
LukJ. W. (2002). Identifying terrorists: Privacy rights in the United States and the United Kingdom. Hastings International and Comparative Law Review, 25(2), 223–259.
17.
MackA.RockI. (1998). Inattentional blindness. Cambridge, MA: MIT Press.
18.
MaroisA.LafondD.VachonF.HarveyE. R.MartinB.TremblayS. (2020). Mobile real-time eye-tracking for gaze-aware security surveillance support systems. In AhramT.KarwowskiW.VergnanoA.LealiF.TaiarR. (Eds.), Advances in Intelligent Systems and Computing: Vol. 1131. Intelligent Human Systems Integration (pp. 201–207). Cham, Switzerland: Springer Nature.
19.
MaroisA.VachonF. (2018). Can pupillometry index auditory attentional capture in contexts of active visual processing?Journal of Cognitive Psychology, 30(4), 484–502.
20.
McGuinnessB. (2004, June). Quantitative analysis of situational awareness (QUASA): Applying signal detection theory to true/false probes and self- ratings. Command and Control Research and Technology Symposium: The Power of Information Age Concepts and Technologies, San Diego, California.
21.
McIntireL.McKinleyR. A.McIntireJ.GoodyearC.NelsonJ. (2013). Eye Metrics: An alternative vigilance detector for military operators. Military Psychology, 25(5), 502–513.
22.
PeysakhovichV.VachonF.VallièresB. R.DehaisF.TremblayS. (2015). Pupil dilation and eye movements can reveal upcoming choice in dynamic decision-making. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 59, 210–214.
23.
St. JohnM.RisserM. R. (2009). Sustaining vigilance by activating a secondary task when inattention is detected. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 53, 155–159.
24.
TaylorP.BilgrienN.HeZ.SiegelmannH. T. (2015). EyeFrame: Real- time memory aid improves human multitasking via domain-general eye tracking procedures. Frontiers in ICT, 2, 17.
25.
TremblayS.LafondD.ChamberlandC.VachonF. (2018). Gaze-aware cognitive assistant for multiscreen surveillance. In KarwowskiW.AhramT. (Eds.), Advances in Intelligent Systems and Computing: Vol. 722. Intelligent Human Systems Integration (pp. 230–236). Cham, Switzerland: Springer Nature.
26.
TrosciankoT.HolmesA.StillmanJ.MirmehdiM.WrightD.WilsonA. (2004). What happens next? The predictability of natural behaviour viewed through CCTV cameras. Perception, 33(1), 87–101.
27.
VachonF.VallièresB. R.SussJ.ThériaultJ.-D.TremblayS. (2016). The CSSS microworld: A gateway to understanding and improving CCTV security surveillance. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 60, 265–269.
28.
ValeraM.VelastinS. A. (2004). Intelligent distributed surveillance systems: A review. IEE Proceedings, 152(2), 193–204.
29.
van VoorthuijsenG.van HoofH.KlimaM.RoubikK.BernasM.PataP. (2005). CCTV effectiveness study. In Proceedings of the 39th IEEE annual 2005 international Carnahan conference on security technology (pp. 105–108). IEEE.
30.
WangX. (2013). Intelligent multi-camera video surveillance: A review. Pattern Recognition Letters, 34(1), 3–19.
31.
WellsH.AllardT.WilsonP. (2006). Crime and CCTV in Australia: Understanding the relationship. Centre of Applied Psychology and Criminology, Faculty of Humanities and Social Sciences. Bond University, Australia.
32.
WickensC. D.HollandsJ. G.BanburyS.ParasuramanR. (2015). Engineering psychology and human performance (4th edition). New York, NY: Routledge.
