Feature correspondence in low quality CCTV videos

Abstract

Closed-circuit television cameras are used extensively to monitor streets for the security of the public. Whether passively recording day-to-day life, or actively monitoring a developing situation such as public disorder, the videos recorded have proven invaluable to police forces world wide to trace suspects and victims alike. The volume of video produced from the array of camera covering even a small area is large, and growing in modern society, and post-event analysis of collected video is a time consuming problem for police forces that is increasing. Automated computer vision analysis is desirable, but current systems are unable to reliably process videos from CCTV cameras. The video quality is low, and computer vision algorithms are unable to perform sufficiently to achieve usable results. In this chapter, we describe some of the reasons for the failure of contemporary algorithms and focus on the fundamental task of feature correspondence between frames of video – a well-studied and often considered solved problem in high quality videos, but still a challenge in low quality imagery. We present solutions to some of the problems that we acknowledge, and provide a comprehensive analysis where we demonstrate feature matching using a 138-dimensional descriptor that improves the matching performance of a state-of-the-art 384-dimension colour descriptor with just 36% of the storage requirements.