Adaptive multiclass correlation filters and its applications in the time series recognition

The adaptive multiclass correlation filters (AMCF) method is proposed to exploit different kinds of features and information in a unified framework for recognition. Theoretical investigation into AMCF shows that it obtains a closed-form subsolution to constrain the optimization objective, simplifying the entire inference mechanism in the multiclass classification. The time series recognition problems, such as human action recognition and radar behavior recognition, are important yet challenging tasks. However, it is still time-consuming to acquire enough labeled training samples. AMCF is capable to exploit different kinds of features to solve the time series recognition problem. With this new correlation filters-based method, we extend the original signals and handle the insufficient training set effectively. Experiments are done on the depth image based action recognition and radar behavior recognition with a small number of training examples, including MSRAction3D, MSRGesture3D, UTD-MHAD, and radar behavior datasets. Particularly, we demonstrate that the proposed action recognition system is based on the completed local binary patterns and AMCF, and successfully achieves superior performances over the state-of-the-arts.