动静态信息融合及动态贝叶斯网络的步态识别

步态是远距离视频监控领域最具潜力的生物特征。目前对步态的识别研究大都是考虑单一条件下步态的识别率,但在穿外套、背包等混合条件下识别率较低,通过分析人体行走时步态的时序特征,提出一种基于动静态信息相结合的多信息融合的动态贝叶斯网络(DSIF-DBN)。模型含有3层状态,模型中每个时间片都为静态信息和动态信息的融合。此模型能很好地表达步态的时序特性,即步态行走时人体姿态,运动幅度等特征的节奏性变化。实验结果表明该方法有较高的识别率,能有机地融合步态的静态信息及动态信息,并且在有噪声及信息缺失的情况下有较好的鲁棒性,大大降低了外套及背包对步态识别的影响。

Gait recognition based on dynamic & static information fusion and dynamic bayesian network

Gait is an important biological characteristics in the long distance video surveillance field. Nowadays, almost all gait recognition researcher focus on gait recognition only under one single condition.However, the gait recognition rate rapidly decline in blended conditions, for example when somebody is wearing a coat or carrying a bag. Based on our analysis of the gait timing characteristics during the human movements,we propose a new gait recognition approach that expresses dynamic information and static information by using a dynamic Bayesian networw(DSIF-DBN). The DSIF-DBN contains three levels of states and for every time slice of the DSIF-DBN model is expressed by the fusion of dynamic information and static information . This model can exectly express the timing characteristics of the gait, which are the body posture and the range of motion, as well as other gait rhythmic change characteristics. Experimental result show that the DSIF-DBN model recognizes gait with high rates and good robustness to noise and lost of information. The DSIF-DBN model can fuse the dynamic information as well as static information and can greatly reduce the impact of gait recognition rates when somebody is wearing a coat or carrying a bag.