Action Recognition Based on Multi-Level Representation of 3D Shape

A novel algorithm is proposed in this thesis for recognizing human actions using a combination of two shape descriptors, one of which is a 3D Euclidean distance transform and the other based on the Radon transform. This combination captures the necessary variations from the space time shape for recognizing actions. The space time shapes are created by the concatenation of human body silhouettes across time. The comparisons are done against some common shape descriptors such as the zernike moments and Radon transform. This is also compared with an algorithm which uses the same concept of a space time shape and uses another shape descriptor based on the Poisson's equation. The proposed algorithm uses a 3D Euclidean distance transform to represent the space time shape and this shape descriptor in comparison to the Poisson's equation based shape descriptor is less complex. By taking the gradient of this distance transform, the space time shape can be divided into different levels with each level representing a coarser version of itself. Then, at each level, specific features such as the R-Transform feature set and the R-Translation vector set are extracted and concatenated to form the action features. These action features extracted from a space time shape of a test sequence are compared with the action features of space time shapes of the training sequences using the minimum Euclidean distance metric and they are classified using the nearest neighbor approach. The algorithm is tested on the Weizmann action database which consists of 90 video sequences of which 10 different actions are performed by 9 different people. Research work is being done to improve the recognition accuracy by extracting features which are more localized and classifying them using a more sophisticated technique.