Semi-automated CCTV surveillance: the effects of system confidence, system accuracy and task complexity on operator vigilance, reliance and workload.

Recent advances in computer vision technology have lead to the development of various automatic surveillance systems, however their effectiveness is adversely affected by many factors and they are not completely reliable. This study investigated the potential of a semi-automated surveillance system to reduce CCTV operator workload in both detection and tracking activities. A further focus of interest was the degree of user reliance on the automated system. A simulated prototype was developed which mimicked an automated system that provided different levels of system confidence information. Dependent variable measures were taken for secondary task performance, reliance and subjective workload. When the automatic component of a semi-automatic CCTV surveillance system provided reliable system confidence information to operators, workload significantly decreased and spare mental capacity significantly increased. Providing feedback about system confidence and accuracy appears to be one important way of making the status of the automated component of the surveillance system more 'visible' to users and hence more effective to use.

[1]  Dariu Gavrila,et al.  The Visual Analysis of Human Movement: A Survey , 1999, Comput. Vis. Image Underst..

[2]  J. G. Hollands,et al.  Engineering Psychology and Human Performance , 1984 .

[3]  David C. Hogg Model-based vision: a program to see a walking person , 1983, Image Vis. Comput..

[4]  Arien Mack,et al.  Change blindness and priming: When it does and does not occur , 2006, Consciousness and Cognition.

[5]  Victor A. Riley,et al.  Operator reliance on automation: Theory and data. , 1996 .

[6]  Shaogang Gong,et al.  Non-intrusive Person Authentication for Access Control by Visual Tracking and Face Recognition , 1997, AVBPA.

[7]  Anthony J. Aretz The Design of Electronic Map Displays , 1991 .

[8]  Sergio A. Velastin,et al.  Intelligent distributed surveillance systems: a review , 2005 .

[9]  Jacob A. Hyman Computer Vision Based People Tracking for Motivating Behavior in Public Spaces , 2003 .

[10]  Susan G. Hill,et al.  Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary? In A , 1989 .

[11]  J. Schooler,et al.  Verbal overshadowing of visual memories: Some things are better left unsaid , 1990, Cognitive Psychology.

[12]  T. W. van der Mark,et al.  Determination of the functional residual capacity during exercise. , 1980, Ergonomics.

[13]  Christopher D. Wickens,et al.  A model for types and levels of human interaction with automation , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[14]  Janet M. Noyes,et al.  People in control: human factors in control room design , 2001 .

[15]  Mark W. Becker,et al.  Volatile visual representations: Failing to detect changes in recently processed information , 2002, Psychonomic bulletin & review.

[16]  M R Endsley,et al.  Level of automation effects on performance, situation awareness and workload in a dynamic control task. , 1999, Ergonomics.

[17]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[18]  Sergio A. Velastin,et al.  A motion-based image processing system for detecting potentially dangerous situations in underground railway stations , 2006 .

[19]  Neil A. Thacker,et al.  Performance characterization in computer vision: A guide to best practices , 2008, Comput. Vis. Image Underst..

[20]  J Sauer,et al.  Prospective memory: a secondary task with promise. , 2000, Applied ergonomics.

[21]  G. A. Miller THE PSYCHOLOGICAL REVIEW THE MAGICAL NUMBER SEVEN, PLUS OR MINUS TWO: SOME LIMITS ON OUR CAPACITY FOR PROCESSING INFORMATION 1 , 1956 .

[22]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[23]  C. Norris,et al.  CCTV: Beyond Penal Modernism? , 2006 .

[24]  William N. Dember,et al.  VIGILANCE AND WORKLOAD IN AUTOMATED SYSTEMS. , 1996 .

[25]  Lu Wang,et al.  Developing Human-Machine Interfaces to Support Appropriate Trust and Reliance on Automated Combat Identification Systems (Developpement d'Interfaces Homme-Machine Pour Appuyer la Confiance dans les Systemes Automatises d'Identification au Combat) , 2007 .

[26]  R. Armitage To CCTV or not to CCTV?: A review of current research into the effectiveness of CCTV systems in reducing crime , 2002 .

[27]  Michael P. Pound,et al.  Quantitative and Qualitative Evaluation of Visual Tracking Algorithms using Statistical Tests , 2007 .

[28]  Christopher D. Wickens,et al.  Automation Reliability in Unmanned Aerial Vehicle Control: A Reliance-Compliance Model of Automation Dependence in High Workload , 2006, Hum. Factors.

[29]  Martina Angela Sasse,et al.  “Not the Usual Suspects”: A study of factors reducing the effectiveness of CCTV , 2008 .

[30]  David Harris,et al.  Engineering Psychology and Cognitive Ergonomics , 2014, Lecture Notes in Computer Science.

[31]  G. Armstrong,et al.  The Maximum Surveillance Society: The Rise of CCTV , 1999 .

[32]  Hai Tao,et al.  Special issue on video surveillance research in industry and academia , 2008, Machine Vision and Applications.

[33]  James P. Bliss,et al.  Investigation of Alarm-Related Accidents and Incidents in Aviation , 2003 .

[34]  Ted Megaw,et al.  The definition and measurement of mental workload , 2005 .

[35]  Brian C. Lovell,et al.  Intelligent CCTV for Mass Transport Security: Challenges and Opportunities for Video and Face Processing , 2007 .

[36]  Michael J. Brooks,et al.  A Stochastic Approach to Tracking Objects Across Multiple Cameras , 2004, Australian Conference on Artificial Intelligence.

[37]  Renwick E. Curry,et al.  Flight-deck automation: promises and problems , 1980 .

[38]  Sergio A. Velastin,et al.  How close are we to solving the problem of automated visual surveillance? , 2008, Machine Vision and Applications.

[39]  G. Davies,et al.  Closed-circuit television: how effective an identification aid? , 2000, British journal of psychology.

[40]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[41]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[42]  R. Parasuraman,et al.  Detecting threat-related intentional actions of others: effects of image quality, response mode, and target cuing on vigilance. , 2009, Journal of experimental psychology. Applied.