基于Petri网和BPNN的多重触控手势识别

Multi-Touch Gesture Recognition Based on Petri Net and Back Propagation Neural Networks

为解决多重触控技术的手势识别问题,提出一个多重触控手势描述与识别框架,给出其描述和识别方法.多重触控手势可分为原子手势和组合手势,在手势描述过程中,利用BP网络对原子手势进行建模,然后在将用户的意图映射为原子手势逻辑、时序和空间关系关联而成的组合手势,并在Petri网引入逻辑、时序和空间关系描述符对组合手势进行描述.在手势识别过程中,根据BP网络分类器检测出原子手势,并触发组合手势Petri网模型的转移,实现组合手势的识别.实验结果表明该方法对不同用户操作习惯有鲁棒性,能有效解决多重触控手势识别问题.

To recognize gestures of multi-touch system, a framework recognition is proposed. Multi-touch gesture can be decomposed including gesture description and into atomic gestures and composite gestures. For gesture description, the back propagation neural networks （BPNN） is used to model the atomic gesture. Users＂ motions are mapped into composite gestures combined with atomic gestures logic, temporal and spatial relations. Petri nets （PN） introduced with logical, temporal, spatial descriptors are used to model the composite gesture. For recognition, BPNN is used as a classifier to recognize atomic gestures and the recognition results trigger the transition of Petri nets for composite gestures to realize the recognition. Experimental results show that the proposed method is robust to different users＂ customs and can recognize multi-touch gestures effectively.