A performance evaluation of several state-of-the-art correlation filters within the context of target tracking is presented. The filters are tested using an introduced algorithm that is adapted online using information of current and past scene frames of the scene. The algorithm achieves a high-rate operation by focusing signal processing on a small fragment of the scene in each frame. The correlation filters are tested using several video test sequences that contain geometric modifications of the target, partial occlusions and clutter. The performance of the tested filters is characterized in terms of detection efficiency, tracking accuracy, and computational complexity using objective metrics.
Facial recognition is a difficult task due to variations in pose and facial expressions, as well as presence of noise and clutter in captured face images. In this work, we address facial recognition by means of composite correlation filters designed with multi-objective combinatorial optimization. Given a large set of available face images having variations in pose, gesticulations, and global illumination, a proposed algorithm synthesizes composite correlation filters by optimization of several performance criteria. The resultant filters are able to reliably detect and correctly classify face images of different subjects even when they are corrupted with additive noise and nonhomogeneous illumination. Computer simulation results obtained with the proposed approach are presented and discussed in terms of efficiency in face detection and reliability of facial classification. These results are also compared with those obtained with existing composite filters.