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A Novel Approach for Fast Action Recognition using Simple Features

We propose a new method for human action recognition from video streams that is fast and robust to random noise, partial occlusions and large changes in camera views. We extract features in the Fourier domain using the bounding boxes containing the silhouettes of a human for a number of frames representing an action. After preprocessing, we divide each space-time volume into space-time sub-volumes and compute their corresponding mean-power spectra as our feature vectors. Our features result in high classification performance using a weighted variant of the Euclidean distance. We require no camera calibration or synchronization and make use of multiple cameras to enrich the training data towards view-invariance. We test the robustness of our method using a variety of experiments including synthetic data generated in a virtual environment and real-world data used by other researchers. We also provide an experimental comparison, using the same data, between our method and two recent alternatives.