6,441 research outputs found
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
BVI-VFI: A Video Quality Database for Video Frame Interpolation
Video frame interpolation (VFI) is a fundamental research topic in video
processing, which is currently attracting increased attention across the
research community. While the development of more advanced VFI algorithms has
been extensively researched, there remains little understanding of how humans
perceive the quality of interpolated content and how well existing objective
quality assessment methods perform when measuring the perceived quality. In
order to narrow this research gap, we have developed a new video quality
database named BVI-VFI, which contains 540 distorted sequences generated by
applying five commonly used VFI algorithms to 36 diverse source videos with
various spatial resolutions and frame rates. We collected more than 10,800
quality ratings for these videos through a large scale subjective study
involving 189 human subjects. Based on the collected subjective scores, we
further analysed the influence of VFI algorithms and frame rates on the
perceptual quality of interpolated videos. Moreover, we benchmarked the
performance of 33 classic and state-of-the-art objective image/video quality
metrics on the new database, and demonstrated the urgent requirement for more
accurate bespoke quality assessment methods for VFI. To facilitate further
research in this area, we have made BVI-VFI publicly available at
https://github.com/danier97/BVI-VFI-database
Space Station Human Factors Research Review. Volume 4: Inhouse Advanced Development and Research
A variety of human factors studies related to space station design are presented. Subjects include proximity operations and window design, spatial perceptual issues regarding displays, image management, workload research, spatial cognition, virtual interface, fault diagnosis in orbital refueling, and error tolerance and procedure aids
Machine Learning for Multimedia Communications
Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learningoriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise
Developing a library of display effects on pilot performance: Methods, meta-analyses, and performance estimates
The design of NextGen and current-day cockpit displays are critical for efficient pilot performance and situation awareness on the flight deck. Before deployment of a design into the cockpit the costs and benefits that a display design imposes on performance and situation awareness should be considered. In this thesis, a design tool was developed to support the design of NextGen displays for situation awareness and performance. This design tool is a library of pilot performance estimates. Through literature reviews and meta-analyses of empirical data, the library was developed to provide display designers 1) qualitative distinctions of display properties that either support or limit full situation awareness, and 2) quantitative performance time estimates until situation awareness as a function of various display formats. A systematic method was also developed for future augmentation of the library
Deep Learning Approach for Seamless Navigation in Multi-View Streaming Applications
Quality of Experience (QoE) in multi-view streaming systems is known to be severely affected
by the latency associated with view-switching procedures. Anticipating the navigation intentions of the
viewer on the multi-view scene could provide the means to greatly reduce such latency. The research work
presented in this article builds on this premise by proposing a new predictive view-selection mechanism.
A VGG16-inspired Convolutional Neural Network (CNN) is used to identify the viewer’s focus of attention
and determine which views would be most suited to be presented in the brief term, i.e., the near-term viewing
intentions. This way, those views can be locally buffered before they are actually needed. To this aim, two
datasets were used to evaluate the prediction performance and impact on latency, in particular when compared
to the solution implemented in the previous version of our multi-view streaming system. Results obtained
with this work translate into a generalized improvement in perceived QoE. A significant reduction in latency
during view-switching procedures was effectively achieved. Moreover, results also demonstrated that the
prediction of the user’s visual interest was achieved with a high level of accuracy. An experimental platform
was also established on which future predictive models can be integrated and compared with previously
implemented models.info:eu-repo/semantics/publishedVersio
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