Advanced correlation filters (CFs) were introduced over three decades ago to offer distortion-tolerant object recognition and are used in applications such as automatic target recognition (ATR) and biometric recognition. Some of the advances in CF design include minimum average correlation energy (MACE) filters that produce sharp correlations and offer excellent discrimination, optimal tradeoff synthetic discriminant function (OTSDF) filters that allow the filter designer to control the tradeoff between peak sharpness and noise tolerance, maximum average correlation height (MACH) filter that removes correlation peak constraints to reduce filter design complexity and quadratic correlation filters (QCFs) that extend the linear CFs to include second-order nonlinearity. In this paper, we summarize two recent major advances in CF design. First is the introduction of maximum margin correlation filters (MMCFs) that combine the excellent localization properties of CFs with the very good generalization abilities of support vector machines (SVMs). Second is the introduction of zero-aliasing correlation filters (ZACFs) that eliminate the aliasing in CF design due to the circular correlation caused by the use of discrete Fourier transforms (DFTs).
To automate video surveillance systems, algorithms must be developed to automatically detect and classify basic human actions. Many traditional approaches focus on the classification of actions, which usually assumes prior detection, tracking, and segmentation of the human figure from the original video. On the other hand, action detection is a more desirable paradigm, as it is capable of simultaneous localization and classification of the action. This means that no prior segmentation or tracking is required, and multiple action instances may be detected in the same video. Correlation filters have been traditionally applied for object detection in images. In this paper, we report the results of our investigation using correlation filters for human action detection in videos. Correlation filters have previously been explored for action classification, but this is the first time they are evaluated for the more difficult task of action detection. In addition, we investigate several practical implementation issues, including parameter selection, reducing computational time, and exploring the effects of preprocessing and temporal occlusion (i.e., loss of video frames) on performance.