Biblio

Security and monitoring systems are more and more demanding in terms of quality, reliability and flexibility especially those dedicated to video surveillance. The quality of the acquired video signal strongly affects the performance of the high level tasks such as visual tracking, face detection and recognition. The design of a video quality assessment metric dedicated to this particular application requires a preliminary study on the common distortions encountered in video surveillance. To this end, we present in this paper a dataset dedicated to video quality assessment in the context of video surveillance. This database consists of a set of common distortions at different levels of annoyance. The subjective tests are performed using a classical pair comparison protocol with some new configurations. The subjective results obtained through the psycho-visual tests are analyzed and compared to some objective video quality assessment metrics. The preliminary results are encouraging and open a new framework for building smart video surveillance based security systems.

Video-based surveillance systems often suffer from poor-quality video in an uncontrolled environment. This may strongly affect the performance of high-level tasks such as visual tracking, abnormal event detection or more generally scene understanding and interpretation. This work aims to demonstrate the impact and the importance of video quality in video surveillance systems. Here, we focus on the most important challenges and difficulties related to the perceptual quality of the acquired or transmitted images/videos in uncontrolled environments. In this paper, we propose an architecture of a smart surveillance system that incorporates the perceptual quality of acquired scenes. We study the behaviour of some state-of-the-art video quality metrics on some original and distorted sequences from a dedicated surveillance dataset. Through this study, it has been shown that some of the state-of-the-art image/video quality metrics do not work in the context of video-surveillance. This study opens a new research direction to develop the video quality metrics in the context of video surveillance and also to propose a new quality-driven framework of video surveillance system.