Carl S. Young, in The Science and Technology of Counterterrorism, 2015
CCTV system coverage.
a.Recognition-level resolution.
b.360° perimeter viewing
c.20-lux light intensity.
2.Guarding.
a.Perimeter checks or monitoring of CCTV at regular intervals (24/7).
b.Updated training and appropriate security certifications.
3.Building façade features.
a.Low-profile.
b.Solid construction material.
c.Set back from the street.
d.Limited number of exterior windows.
4.Entry door alarm in case of disruptions to the 24/7 guarding operation.
Carl S. Young, in The Science and Technology of Counterterrorism, 2015
A CCTV camera is not of much use if it is unable to image the required area as dictated by the operational requirements. If full-area coverage of a lobby is required, a camera or series of cameras must be able to image the entire scene. The lens of the camera, the dimensions of the optical sensor (CCD), and the distance of the lens from the object determine the field-of-view. The CCD is to a CCTV camera what the retina is to the human eye.
The angle-of-view of a camera lens determines the angle of acceptance of the imaged scene along the relevant dimension of the CCD sensor (i.e., horizontal or vertical). It is also important in determining the camera resolution because it relates to the density of pixels.
The angle-of-view is typically expressed in radians. There are 2π radians in a complete circle, which can also be characterized in terms of degrees. As the reader is probably aware, there are 360 degrees in a circle. To convert radians into degrees, multiply the number of radians (rad) by 180/π. For example an angle of 1 rad is roughly equal to 180/3.14 ~57 degrees.
It can be shown by geometric arguments that an approximate expression for the angle-of-view equals the CCD sensor dimension (CCDw), divided by the lens focal length (f), or CCDw/f. In other words, the angle-of-view is a function of the relevant dimension of the sensor (i.e., horizontal or vertical) divided by the lens focal length. CCDw is often fixed, so the variable in determining the angle-of-view defaults to the lens focal length.
For example, a standard dimension for a CCTV camera CCD is 1/3 in. A 1/3-in CCD has a horizontal dimension of 4.4 mm. We will later observe that the number of pixels-per-foot across the horizontal angle-of-view is a key specification for camera resolution. If the lens focal length is 3.5 mm and the camera uses a 1/3-in CCD, then the horizontal angle-of-view of the lens is given by 4.4 mm/3.5 mm = 1.25 rad or 72 degrees.
Similarly, if the focal length is 10 mm, the horizontal angle-of-view is 0.44 rad (i.e., 4.4 mm/10 mm) or 25 degrees. Increasing the focal length decreases the angle-of-view and decreasing the focal length does the opposite. This is a very important relationship in understanding CCTV system performance relative to satisfying security operational requirements. Figure 11.4 depicts the angle-of-view as seen by a CCTV camera lens.
Figure 11.4. CCTV angle-of-view.
(Source: Federal Highway Administration. “Sensor-Installation Techniques.” Traffic Detector Handbook. Third Edition—Volume II. http://www.fhwa.dot.gov/publications/research/operations/its/06139/chapt5d.cfm.)Figure 11.5 illustrates this important relationship between focal length and the angle-of-view. Understanding this inverse relationship is critical when designing a security system to monitor a doorway as opposed to a parking lot.
Figure 11.5. Field-of-view and focal length.
(Used with permission from A. S. Security & Surveillance. http://www.assecurity.ca/.)As noted above, the dimensions of the CCD sensor and the focal length determine the angle-of-view. Table 11.3 specifies the difference in the angle-of-view in the horizontal direction as a function of focal length and CCD sensor size. The data confirms that the angle-of-view decreases with increasing focal length and decreasing chip size.
Table 11.3. Camera CCD Chip Size and Approximate Angles-of-View (degrees)
| 2.0 | - | - | - | 82 |
| 2.8 | - | - | 86 | 57 |
| 4.0 | - | 77 | 67 | 47 |
| 4.8 | 83 | 67 | 57 | 40 |
| 6.0 | 70 | 56 | 48 | 32 |
| 8.0 | 56 | 44 | 36 | 25 |
| 12 | 39 | 30 | 25 | 17 |
| 16 | 30 | 23 | 17 | 13 |
| 25 | 18 | 15 | 12 | 8 |
| 50 | 10 | 7 | 6 | 4 |
(Data from http://www.ezcctv.com/.)
A closely related concept is the field-of-view. This is the actual width (if using the horizontal dimension of the sensor) of the scene subtended by the angle-of-view. The field-of-view equals the angle-of-view times the distance of the camera lens from the object. Figure 11.6 shows a series of photographs that gives an appreciation for the effect of both focal length and distance from lens-to-object on the total field-of-view.
Figure 11.6. The effect of focal length and distance.
(Reproduced with permission from EZCCTV.com.)Fortunately one need not memorize camera focal lengths to select the appropriate CCTV camera, but it is helpful to understand that it is a combination of the lens focal length, the distance from the lens to the object, and the CCD sensor size that determines the field-of-view, a crucial CCTV feature with significant security implications.
The operational implications of the field-of-view are obvious. If the field-of-view is too narrow, there is a risk of missing a risk-relevant event. If the field-of-view is too wide, the resolution may be insufficient to meet a scenario-specific operational requirement (e.g., facial recognition).
Online calculators exist to help with CCTV system design (e.g., http://www.jvsg.com/online/#), and these can take some of the pain out of the process. However, it is instructive to at least be aware of the theory behind these calculations so that one appreciates that changing certain parameters can greatly affect system performance for a given scenario. The ability to do back-of-the-envelope calculations is especially handy if an online calculator is inaccessible.
John Wood, in Meeting Diversity in Ergonomics, 2007
The following sizes of CCTV image were compared in the trial at a 700 mm viewing distance (all at 4:3 aspect ratio):
•smallest: 30 mm × 40 mm;
•standard: 41 mm × 54 mm;
•nontuple: 91 mm × 121 mm;
•native: 163 mm × 217 mm;
•full screen: 284 mm × 380 mm.
To compare how operator performance might be affected by presentation format (i.e. single image or multiples), two multiplexed layouts were developed (see Fig. 4). The same image size (91 mm × 121 mm) was used to compare single, quadruple (2 × 2) and nontuple (3 × 3) image formats. To enable a comparison of performance between different display types, all conditions were repeated for all subjects on both a 19″ CRT monitor and a flat screen 19″ TFT monitor.
FIGURE 4. Image presentation formats showing single, quadruple and nontuple image formats.
In order to compare performance with different input devices, the subjects repeated all conditions using both a mouse and a keyboard. A realistic target detection task was used to compare operator performance with different image sizes, presentation formats and display types. The subjects were asked to inspect several series of CCTV images and simply decide whether or not a target vehicle on the hard shoulder was present in each.
Live CCTV feeds from appropriate CCTV cameras, with fields of view specified for ATM, were not available and hence representative video footage was captured during day and night (under motorway lighting) conditions. The video was captured using a digital camcorder, transferred to PC and edited into 20 s clips. Two types of clips were captured:
•without a vehicle on the hard shoulder; and
•with a vehicle on the hard shoulder (stimulus clips).
The position of the target vehicle on the hard shoulder varied but was shown mainly at the far point of the field of view – the worst case scenario where the target is at its smallest. In practice, the position of vehicles within the camera field of view would be entirely random.
Operator performance was primarily measured by assessing error rate, i.e. failures to correctly identify whether an image included a vehicle on the hard shoulder or not.
Additionally, time taken to complete conditions was recorded in order to provide a basis upon which the time required to open sections and links was estimated.
A repeated measures study design was employed, in which each subject experienced each of 56 test conditions. The presentation sequence was randomized by means of a Latin Square in order to minimize potential order effects.
Each subject inspected 30 CCTV video clips for each of the 56 test conditions. As previously described there were two types of clip:
•non-stimulus (empty hard shoulder); and
•stimulus (clip includes a vehicle stopped on the hard shoulder).
A bespoke software program was developed to manage the video clip presentation and randomization. Stimulus clips were presented randomly in the sequence of non-stimulus clips at an approximate ratio of 1:10. This ratio for target images is probably higher than might be expected in reality. However, with a practical need to capture real data, it was essential to present a sufficient number of targets under each test condition.
Subjects were required to view each image presented and judge whether the hard shoulder was clear or whether a target (vehicle) was present. Subject responses and errors were recorded by the computer system controlling the presentation.
With a total of 56 different test conditions per subject, and with each condition requiring 30 video clips, each subject viewed a total of 1680 clips. The large number of clips presented to the subject in one sitting was designed to force errors; by presenting lots of stimuli, the chances for error were increased, thereby ensuring that some error scores were available for analysis.
Ten full-time operators (eight male and three female aged 40–64) from a District Council CCTV control centre took part in the study. Whilst unfamiliar with motorway monitoring and management, these subjects were familiar with searching CCTV images in a professional capacity and represented a relatively homogenous group in terms of training and expertise.
To provide a level of task realism, the video clips were presented in clusters of 10 as an approximate match to the number of images that might be required to open sections. Following a familiarization run, each subject ran through the full trial sequence. The trial was divided into two sessions, each consisting of 28 randomly presented conditions, with one session using the TFT monitor and the other the CRT monitor. The order in which monitors were used was randomized between subjects.
Subjects inspected each video clip in turn (highlighted by the software with a green ‘bounding box’) and decided whether or not a vehicle was present on the hard shoulder (Fig. 5), confirming their decision by keyboard button press or mouse click (depending on trial conditions).
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