Gait recognition based on dynamic & static information fusion and dynamic bayesian network
动静态信息融合及动态贝叶斯网络的步态识别

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.