Real-time augmented reality application using color analysis

This paper proposes a new interactive augmented reality (AR) application for tracking a remote-control car controlled by players. We present it as a markerless framework for tracking colored remote-control car by integrating a Bayesian classifier into particle filters. This adds the useful abilities of automatic track initialization and recovery from tracking failures in a dynamic background. Furthermore, by using the online adaptation of color probabilities, this method is able to cope well with illumination changes. We calculate the projection matrix as an online process. The method presented can be used to develop the application of AR to remote-control car playing. The aforementioned application can entertain players interactively by controlling the car to the augmented items.