Recognition of human actions from video sequences is a very popular in Computer Vision. Since an action takes place in 3-D, and is projected on a sequence of 2-D images, the projected 2-D motion may vary depending on the viewpoint of the camera. This creates a problem in recognizing human actions from 2D video sequence. In most current works on action recognition, the issue of view-invariance has been ignored. In this talk, I will present our work on human action recognition which uses geometry to deal with the problem of view invariance.

Object recognition is a classic problem in computer vision, which has been popular in the community for the last thirty years. In the second part of my talk, I will present   a novel multi-view generic object class recognition method based on 3D object modeling. Instead of using a complicated mechanism for relating multiple 2D training views, the proposed method establishes spatial connections between these views by attaching appearance features to the surfaces of 3D models. The 3D model is represented by a volume consisting of binary slices, and is generated by using a new homographic framework.

When an actor performs an action in 3D, the points on the outer boundary of the actor are projected as 2D (x, y) contour in the image plane. A sequence of such 2D contours with respect to time generates a spatiotemporal volume (STV) in (x, y, t), which can be treated as 3D object in the (x, y, t) space. In the third part of this talk, I will present our approach for human recognition by treating actions as objects. We analyze STV by using the differential geometric surface properties, such as peaks, pits, valleys and ridges, which are important action descriptors capturing both spatial and temporal properties. A set of motion descriptors for a given action is called an action sketch. The action descriptors are related to various types of motions and object deformations.