Unified framework for human activity recognition: An approach using spatial edge distribution and ℜ-transform

In this paper, a unified approach for the recognition of human activity using the spatial edge distribution of gradients and orientation of the human silhouettes in a video sequence is presented. The spatial edge distribution is computed on still image at different levels of resolution of sub-images to extract out the shape of the activity posture. The fuzzy trapezoidal membership function is used to extract the key frames of the activity, and the single still key image is extracted according to the histogram distance. The temporal content of the activity is extracted by the computation of orientation of the silhouettes using ℜ-transform. The ℜ-transform is applied on the binary human silhouettes, and the extraction of human silhouettes from the video sequence is done using texture based segmentation techniques. The high dimensionality of the ℜ-transform features is handled by applying Local linear embedding (LLE) dimension reduction approach. A unified model is constructed by integrating the spatial edge distribution of gradients and temporal content of the activity. The performance of the developed model is demonstrated on publicly available datasets, and the highest classification accuracy achieved on each datasets is compared with the similar state-of-the-art techniques and shows the superior performance.