Gait recognition based on improved dynamic Bayesian networks

In this paper, we proposed an improved two-level dynamic Bayesian network layered time series model (LTSM), which aims to solve the limitations hindering the application of available dynamic Bayesian networks, the hidden Markov model (HMM) and the dynamic texture (DT) model to gait recognition. In the first level, a gait silhouette or feature cycle is divided into several temporally adjacent clusters. Each cluster is modeled by a DT or logistic DT (LDT). In the second level, HMM is built to describe the relationship among the DTs/LDTs. Besides LTSM, LDT is also an improved dynamic Bayesian network presented in this paper to describe the binary image sequence, which introduces the logistic principle component analysis (PCA) to learning its parameters. We demonstrated the validity of LTSM with experiments on both the CMU Mobo gait database and CASIA gait database (dataset B), and that of LDT on the CMU Mobo gait database. Experimental results showed the superiority of the improved dynamic Bayesian networks.

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