It is estimated that there are five million CCTV cameras in use today. CCTV is used by a wide range of
organisations and for an increasing number of purposes. Despite this, there has been little research to
establish whether these systems are fit for purpose. This thesis takes a socio-technical approach to
determine whether CCTV is effective, and if not, how it could be made more effective. Humancomputer
interaction (HCI) knowledge and methods have been applied to improve this understanding
and what is needed to make CCTV effective; this was achieved in an extensive field study and two
experiments. In Study 1, contextual inquiry was used to identify the security goals, tasks, technology
and factors which affected operator performance and the causes at 14 security control rooms. The
findings revealed a number of factors which interfered with task performance, such as: poor camera
positioning, ineffective workstation setups, difficulty in locating scenes, and the use of low-quality
CCTV recordings.
The impact of different levels of video quality on identification and detection performance was
assessed in two experiments using a task-focused methodology. In Study 2, 80 participants identified
64 face images taken from four spatially compressed video conditions (32, 52, 72, and 92 Kbps). At a
bit rate quality of 52 Kbps (MPEG-4), the number of faces correctly identified reached significance. In
Study 3, 80 participants each detected 32 events from four frame rate CCTV video conditions (1, 5, 8,
and 12 fps). Below 8 frames per second, correct detections and task confidence ratings decreased
significantly.
These field and empirical research findings are presented in a framework using a typical CCTV
deployment scenario, which has been validated through an expert review. The contributions and
limitations of this thesis are reviewed, and suggestions for how the framework should be further
developed are provided