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Vision based referee sign language recognition system for the RoboCup MSL league

机译:RoboCup MSL联赛基于视觉的裁判手语识别系统

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摘要

In RoboCup Middle Size league (MSL) the main referee uses assisting technology, controlled by a second referee, to support him, in particular for conveying referee decisions for robot players with the help of a wireless communication system.In this paper a vision-based system is introduced, able to interpret dynamic and static gestures of the referee, thus eliminating the need for a second one. The referee's gestures are interpreted by the system and sent directly to the Referee Box, which sends the proper commands to the robots. The system is divided into four modules: a real time hand tracking and feature extraction, a SVM (Support Vector Machine) for static hand posture identification, an HMM (Hidden Markov Model) for dynamic unistroke hand gesture recognition, and a FSM (Finite State Machine) to control the various system states transitions. The experimental results showed that the system works very reliably, being able to recognize the combination of gestures and hand postures in real-time. For the hand posture recognition, with the SVM model trained with the selected features, an accuracy of 98,2% was achieved. Also, the system has many advantages over the current implemented one, like avoiding the necessity of a second referee, working on noisy environments, working on wireless jammed situations. This system is easy to implement and train and may be an inexpensive solution.
机译:在RoboCup中型联赛(MSL)中,主要裁判使用辅助技术,该技术由第二个裁判控制,尤其是在无线通信系统的帮助下,为机器人运动员传达裁判决策。引入了系统,能够解释裁判的动态和静态手势,从而无需第二个手势。裁判的手势由系统解释,并直接发送到裁判箱,裁判箱将适当的命令发送给机器人。该系统分为四个模块:实时手部跟踪和特征提取,用于静态手部姿势识别的SVM(支持向量机),用于动态单冲程手势识别的HMM(隐藏马尔可夫模型)和FSM(有限状态)机器)以控制各种系统状态的转换。实验结果表明,该系统工作非常可靠,能够实时识别手势和手势的组合。对于手部姿势识别,通过使用所选功能训练的SVM模型,可以达到98.2%的精度。而且,该系统相对于当前已实现的系统具有许多优势,例如避免了第二个裁判员的必要性,在嘈杂的环境中工作,在无线干扰情况下工作。该系统易于实施和培训,并且可能是一种廉价的解决方案。

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