Estimating performance limits for automatic target recognition in compressed video

Robust real-time recognition of multiple targets with varying pose requires heavy computational loads, which are often too demanding to be performed online at the sensor location. Thus an important problem is the performance of ATR algorithms on highly-compressed video sequences transmitted to a remote facility. We investigate the effects of H.264 video compression on correlation-based recognition algorithms. Our primary test bed is a collection of fifty video sequences consisting of long-wave infrared (LWIR) and mid-wave infrared (MWIR) imagery of ground targets. The targets are viewed from an aerial vehicle approaching the target, which introduces large amounts of scale distortion across a single sequence. Each sequence is stored at seven different levels of compression, including the uncompressed version. We employ two different types of correlation filters to perform frame-by-frame target recognition: optimal tradeoff synthetic discriminant function (OTSDF) filters and a new scale-tolerant filter called fractional power Mellin radial harmonic (FPMRH) filters. In addition, we apply the Fisher metric to compressed target images to evaluate target class separability and to estimate recognition performance as a function of video compression rate. Targets are centered and cropped according to ground truth data prior to separability analysis. We compare our separability estimates with the actual recognition rates achieved by the best correlation filter for each sequence. Numerical results are provided for several target recognition examples.