Continuous manual responses and continuous gaze tracking during closed circuit television (CCTV) monitoring

Christina Howard, Iain Gilchrist, Tom Troscianko, Ardhendu Behera, David Hogg

Research output: Chapter in Book/Report/Conference proceedingConference proceeding (ISBN)peer-review

Abstract

The typical CCTV operator is required to monitor a large number of events to identify potential incidents. This is an active task in which the eyes move from event to event and operators rate the potential risk associated with each event. Here operators watched real CCTV footage and moved a joystick to continuously indicate perceived suspiciousness during continuous gaze recording. By comparing manual responses with between-subjects variability in eye position at each time, we examined the magnitude of the visuo-motor lag. We performed correlations at all possible lags between these two measures and searched for the maximal negative correlation coefficient. The presence of a visuo-motor lag of around one second has been proposed for a range of tasks including motor racing, batting in cricket, making tea and sandwiches and has been suggested to be constant across levels of expertise (see Land, 2006). However, in CCTV monitoring there is a heavy cognitive component to the task. We report both substantially longer lags and an effect of expertise. We propose that these measures provide a novel way to calculate lags across a range of tasks as well as to identify critical spatio-temporal episodes in CCTV footage.
Original languageEnglish
Title of host publicationNot Known
Pages84-84
Publication statusPublished - 2009
EventEuropean Conference on Eye Movements - Southampton, United Kingdom
Duration: 23 Aug 200927 Aug 2009

Conference

ConferenceEuropean Conference on Eye Movements
Country/TerritoryUnited Kingdom
CitySouthampton
Period23/08/0927/08/09

Fingerprint

Dive into the research topics of 'Continuous manual responses and continuous gaze tracking during closed circuit television (CCTV) monitoring'. Together they form a unique fingerprint.

Cite this