3D Pose Tracking Using Optical Flow

Abstract

An action recognition system has many applications, ranging from security to sign language interpretation. If a subject's body pose can be tracked in streams of video, a wealth of information about possible actions can be extracted. In this thesis a three dimensional pose tracking system is presented, designed so that its output can be used as input for an action recognition system. By presenting a classifier with optical flow and low resolution greyscale images, the system is able to detect and track a subject's pose over an extended period of time. Optical flow is the distribution of motion over an image, and was hypothesized to be an important factor in the system's ability to perceive depth. Investigations into the effect of optical flow reveal it to be important for the system's performance, yet less important than other inputs when it comes to depth perception.