Offline quality monitoring for legal evidence images in video-surveillance applications
Video-surveillance attracted an important research effort in the last few years. Many works are dedicated to the design of efficient systems and the development of robust algorithms. video compression is a very important stage in order to ensure the viability of video-surveillance systems. However, it introduces some distortions decreasing significantly the detection, recognition and identification tasks for legal investigators. Fortunately, an important effort is made in terms of standard definition for video-surveillance in order to achieve to a complete interoperability. However, quality issues are still not addressed in an appropriate way. Investigators are often facing the dilemma of selecting the best match (legal evidence) of the targeted object in the video-sequence. In this paper, we propose an offline quality monitoring system for the extraction of most suitable legal evidence images for video-surveillance applications. This system is constructed around three innovative parts: First, a robust tracking algorithm based on foveal wavelet and mean shift. Second, a no-reference quality metric based on sharpness feature. Finally, a super-resolution algorithm allowing to increase the size of the tracked object without using any information outside the image itself. The combination of the proposed algorithms allowed the construction of a quality monitoring system increasing significantly the efficiency of the legal evidence image extraction.
quality monitoring video-surveillance tracking super-resolution sharpness metric