Super resolution recovery for multi-camera surveillance imaging

Abstract

In many surveillance video applications, it is of interest to recognize an object or a person, which occupies a small portion of a low-resolution, noisy video. This paper addresses the problem of super-resolution recovery of a region of interest from more than one low-resolution view of a scene recorded by multiple cameras. The multiple camera scenario alleviates the difficulty in registration of multiple frames of video that contain non-rigid or multiple object motion in the single camera case. With proper temporal registration of multiple videos, arbitrary scene motion can be handled. The success of super-resolution recovery from multiple views in real applications vitally depends on two factors: i) the accuracy of multiple view registration results, and ii) the accuracy of the camera and data acquisition model. We propose a system, which consists of a method for sub-pixel accurate spatio-temporal alignment of multiple video sequences for view registration and the projections onto convex sets method for super-resolution recovery. Experiments were implemented using two commercial analog video cameras, which do not perform on-board compression. Experimental results show that the super resolution recovery of dynamic scenes can be achieved as long as the multiple views of the scene can be registered with sub-pixel accuracy.