Human Action Recognition Using Compact Global Descriptors Derived from 2DPCA-2DLDA

Dense feature representations are recently popular for human action recognition based on video. They achieve better performance than sparse local representations. However, dense representations usually lead to high-dimensional data which contain plenty of redundancy information. In order to solve this issue, a novel compact representation based on global Gist feature and 2DPCA-2DLDA is proposed. Firstly Global grid based Gist features are extracted from image sequences and described as Gist matrices. The Gist matrices reflect global structure distribution of local grids, and at the same time depict frequency characteristics of the local grids. Secondly, bilateral dimensionality reduction is developed to our proposed Gist matrices. The most discriminative spatial structure and frequency characteristic of local grids are separately selected with 2DPCA and 2DLDA. Finally, the derived structure and frequency properties are simultaneously embedded into a low dimensional compact global descriptor. Experimental results on UCF Sports dataset validate that the proposed method makes a good compromise between feature discrimination and dimensions. And the action recognition accuracy is promising.