Robust image-domain target tracking and recognition process under heavy urban background clutter conditions

Abstract

Time-differencing process, with the help of image registration techniques, is useful for image-domain moving target detection under heavy clutter conditions. Time-differencing between two well-registered image frames can significantly suppress the heavy static background clutter, and thus improve moving target detection. However, we may still lose detection of a moving target from time to time under heavy clutter conditions (Pd < 100%), and also we may lose detection of a moving target when this target stops moving. For example, a moving vehicle will temporarily stop moving in front of a red light or a stop sign. In general, the performance of a conventional tracking process depends on the performance of the detection process. In this paper, we present our newly developed image-domain moving target tracking and process using an adaptive local target correlation tracker. Once we started to track a target, the correlation tracker can continue to track this target, no matter whether we can still detect this target in the future image frames or not. Both single and multiple target tracking capabilities using the correlation tracker have been developed. Furthermore, while continuing to track a moving vehicle, we apply a super-resolution image enhancement (SRIE) process developed at SAIC [1] to improve the vehicle resolution and signal-to-noise ratio (SNR) for better automatic target recognition (ATR) or human/pilot-monitored recognition performance.