Applying psychological science to the CCTV review process: a review of cognitive and ergonomic literature

As CCTV cameras are used more and more often to increase security in communities, police are spending a larger proportion of their resources, including time, in processing CCTV images when investigating crimes that have occurred (Levesley & Martin, 2005; Nichols, 2001). As with all tasks, there are ways to approach this task that will facilitate performance and other approaches that will degrade performance, either by increasing errors or by unnecessarily prolonging the process. A clearer understanding of psychological factors influencing the effectiveness of footage review will facilitate future training in best practice with respect to the review of CCTV footage. The goal of this report is to provide such understanding by reviewing research on footage review, research on related tasks that require similar skills, and experimental laboratory research about the cognitive skills underpinning the task. The report is organised to address five challenges to effectiveness of CCTV review: the effects of the degraded nature of CCTV footage, distractions and interrupts, the length of the task, inappropriate mindset, and variability in people’s abilities and experience. Recommendations for optimising CCTV footage review include (1) doing a cognitive task analysis to increase understanding of the ways in which performance might be limited, (2) exploiting technology advances to maximise the perceptual quality of the footage (3) training people to improve the flexibility of their mindset as they perceive and interpret the images seen, (4) monitoring performance either on an ongoing basis, by using psychophysiological measures of alertness, or periodically, by testing screeners’ ability to find evidence in footage developed for such testing, and (5) evaluating the relevance of possible selection tests to screen effective from ineffective screener

Colour image processing and texture analysis on images of porterhouse steak meat

This paper outlines two colour image processing and texture analysis techniques applied to meat images and assessment of error due to the use of JPEG compression at image capture. JPEG error analysis was performed by capturing TIFF and JPEG images, then calculating the RMS difference and applying a calibration between block boundary features and subjective visual JPEG scores. Both scores indicated high JPEG quality. Correction of JPEG blocking error was trialled and found to produce minimal improvement in the RMS difference. The texture analysis methods used were singular value decomposition over pixel blocks and complex cell analysis. The block singular values were classified as meat or non- meat by Fisher linear discriminant analysis with the colour image processing result used as ‘truth.’ Using receiver operator characteristic (ROC) analysis, an area under the ROC curve of 0.996 was obtained, demonstrating good correspondence between the colour image processing and the singular values. The complex cell analysis indicated a ‘texture angle’ expected from human inspection

A statistical reduced-reference method for color image quality assessment

Although color is a fundamental feature of human visual perception, it has been largely unexplored in the reduced-reference (RR) image quality assessment (IQA) schemes. In this paper, we propose a natural scene statistic (NSS) method, which efficiently uses this information. It is based on the statistical deviation between the steerable pyramid coefficients of the reference color image and the degraded one. We propose and analyze the multivariate generalized Gaussian distribution (MGGD) to model the underlying statistics. In order to quantify the degradation, we develop and evaluate two measures based respectively on the Geodesic distance between two MGGDs and on the closed-form of the Kullback Leibler divergence. We performed an extensive evaluation of both metrics in various color spaces (RGB, HSV, CIELAB and YCrCb) using the TID 2008 benchmark and the FRTV Phase I validation process. Experimental results demonstrate the effectiveness of the proposed framework to achieve a good consistency with human visual perception. Furthermore, the best configuration is obtained with CIELAB color space associated to KLD deviation measure