We propose a novel human-computer interface for video gaming systems. Unlike current systems that use active sensors (e.g. Kinect), in the proposed approach, the human-poses are captured by a single camera and used to control the course of the game. This eliminates the need for traditional hand-held controllers and allows for the player to be immersed into the virtual environment of the game. The success of the approach is achieve through a robust extraction of human silhouettes and by a simple but effective human-pose recognizer. The system employs an adaptive framework for background subtraction using Adaptive Local Principal Component Analysis (ALPCA), which is able to handle sudden as well as gradual changes in background. Next, the extracted human silhouettes are used to train a appearance-based algorithm that can recognize the human poses. The excellent results reported here justify the elimination of active sensors and game controllers for many traditional games.

In this paper, a homography-based approach for determining the ground plane using image pairs is presented. Our approach is unique in that it uses a Modified Expectation Maximization algorithm to cluster pixels on images as belonging to one of two possible classes: ground and non-ground pixels. This classification is very useful in mobile robot navigation because, by segmenting out the ground plane, we are left with all possible objects in the scene, which can then be used to implement many mobile robot navigation algorithms such as obstacle avoidance, path planning, target following, landmark detection, etc. Specifically, we demonstrate the usefulness and robustness of our approach by applying it to a target following algorithm. As the results section shows, the proposed algorithm for ground plane detection achieves an almost perfect detection rate (over 99%) despite the relatively higher number of errors in pixel correspondence from the feature matching algorithm used: SIFT.