Transfer Learning and Identification Method of Cross-View Target Trajectory Utilizing HMM

The behavior identification of the target trajectory is one of the important issues in space behavior analysis. Since the target trajectory model obtained from a fixed view cannot be adapted to the change of the observation perspective, it needs to be retrained when being faced with a new view, which leads to a great amount of increment in application cost. This study proposes a hidden Markov model (HMM) based on the cross-view transfer learning and the recognition method that firstly constructs a linear mapping relationship between the observation matrices of the source and target view utilizing the domain trajectory of the HMMs and obtains the observation matrix parameters of the target domain through the mapping system. Secondly, the transfer probability of the source domain is further optimized to obtain the target domain of the HMM and to identify the behavior of the target domain trajectory utilizing a small number of samples from the view of the target domain. The experimental results denote that the proposed method could effectively realize the identification of the trajectory behavior utilizing a small sample size in the target domain and would greatly reduce the application cost of the identification of the cross-view target trajectory.