Investigation into Optical Flow Super-Resolution for Surveillance Applications
Frank C. Lin, Clinton Fookes, Vinod Chandran, Subramanian Sridharan · 2005
Video surveillance systems are becoming an indispensable tool in today's environment, particularly for security related applications. Surveillance footage is often routinely used to identify faces of criminals "caught in the act" or for tracking individuals in a crowded environment. Most face images captured with these systems however, are small and coarse, making it extremely difficult to identify an individual through human observation or via automatic face recognition systems. Super-resolution (SR) is a technique that can overcome this limitation by combining complimentary information from several frames of a video sequence to produce high resolution images of a subject. A problem that plagues many existing SR systems is that they can only deal with simple, rigid inter-frame transformations, thus performing poorly with face images as faces are non-planar, non-rigid, non-lambertian and can self-occlude. This paper presents a SR system to overcome these limitations by using a robust optical flow technique. An investigation into the quality of the super-resolved images and their dependency on the number of video sequence frames used in the reconstruction is undertaken. Different fusion techniques are also investigated and experiments are conducted over two image sequences. Results show significant improvement of the image quality and resolution over the original low resolution sequences.