Abstract
Video transmission and analysis is often utilized in applications outside of the entertainment sector, and generally speaking this class of video is used to perform specific tasks. Examples of these applications include security and public safety. The Quality of Experience (QoE) concept for video content used for entertainment differs significantly from the QoE of surveillance video used for recognition tasks. This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality. Recognizing the growing importance of video in delivering a range of public safety services, we focused on developing critical quality thresholds in license plate recognition tasks based on videos streamed in constrained networking conditions. Since the number of surveillance cameras is still growing it is obvious that automatic systems will be used to do the tasks. Therefore, the presented research includes also analysis of automatic recognition algorithms.
Highlights
The transmission of video is often used for various applications outside of the entertainment sector, and generally this class of video is used to perform specific tasks
We should mention the Liberty Group, an Open Europe organization, the Electronic Frontier Foundation, and the Ethics Board of the FP7-SEC INDECT (INDECT is intelligent information system supporting the observation, search and detection of suspicious or criminal activity in order to protect the security of citizens in an urban environment) [9]
Building a detection probability model for all of the data is difficult, and so we considered a simpler case based on the hypothetical reference circuits (HRC) groups
Summary
The transmission of video is often used for various applications outside of the entertainment sector, and generally this class of video is used to perform specific tasks. In this paper, recognizing the growing importance of video in delivering a range of public safety services, we have attempted to develop critical quality thresholds in license plate recognition tasks, based on video streamed in constrained networking conditions. Experience) for video content used for entertainment is not an option as this QoE differs considerably from the QoE of surveillance video used for recognition tasks This is because, in the latter case, the subjective satisfaction of the user depends on achieving a given functionality (event detection, object recognition). In the area of entertainment video, a great deal of research has been performed on the parameters of the contents that are the most effective for perceptual quality These parameters form a framework in which predictors can be created, so objective measurements can be developed through the use of subjective testing.
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