Pyramidal Zernike Over Time: A Spatiotemporal Feature Descriptor Based on Zernike Moments

摘要

This paper aims at presenting an approach to recognize human activities in videos through the application of Zernike invariant moments. Instead of computing the regular Zernike moments, our technique, named Pyramidal Zernike Over Time (PZOT), creates a pyramidal structure and uses the Zernike response at different levels to associate subsequent frames, adding temporal information. At the end, the feature response is associated to Gabor filters to generate video descriptions. To evaluate the present approach, experiments were performed on the UCFSports dataset using a standard protocol, achieving an accuracy of 86.05%, comparable to results achieved by other widely employed spatiotemporal feature descriptors available in the literature.