A Vision Based Game Control Method
The appeal of computer games may be enhanced by vision-based user inputs. The high speed and low cost requirements for near-term, mass-market game applications make system design challenging. In this paper we propose a vision based 3D racing car game controlling method, which analyzes two fists positions of the player in video stream from the camera to get the direction commands of the racing car.
This paper especially focuses on the robust and real-time Bayesian network (BN) based multi-cue fusion fist tracking method. Firstly, a new strategy, which employs the latest work in face recognition, is used to create accurate color model of the fist automatically. Secondly, color cue and motion cue are used to generate the possible position of the fist. Then, the posterior probability of each possible position is evaluated by BN, which fuses color cue and appearance cue. Finally, the fist position is approximated by the hypothesis that maximizes a posterior. Based on the proposed control system, a racing car game, “Simulation Drive”, has been developed by our group. Through the game an entirely new experience can be obtained by the player.
KeywordsBayesian Network Tracking Algorithm Shift Algorithm Propose Control System Game Control
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