Trajectory Recognition Of Moving Objects Based On Gassian Mixture Hidden Markov Model

In the field of trajectory recognition based Gaussian Mixture Hidden Markov Model (GHMM), the key issues is obtaining trajectory of moving objects, robust presenting and modeling of the trajectory. Furthermore, we analyze activities by the models. Given technical objectives of trajectory recognition, this paper investigates systemically many Algorithms, following the framework of trajectory recognition, such as moving objective detecting, Tracking and trajectory modeling etc. We concentrated on trajectory modeling. We model trajectory by Gaussian Mixture Hidden Markov Model. Before modeling, trajectories are segmented at points of change in curvature, and the subtrajectories are represented by their principal component analysis (PCA) coefficients, to reduce the feature space dimension. Then we present a framework to robustly estimate the multivariate probability density function based on PCA coefficients of the subtrajectories using GHMMs.The Experimental video is from CAVIAR project in the paper, which is obtained from a fixed camera. We can analysis activity of customers'coming and going by trajectory recognition. The customers'activities include walking from left to right, walking from right to left, entering from the left, entering from the right, out from the left and out from the right. This process is divided into three stages. First, we obtain the trajectory through detecting and tracking. Second, we cluster the trajectory into six categories by K-means clustering, and PCA reducing dimensionality is used to process trajectory; Finally, we will establish GHMM for each trajectory, and Validate the validity of the model by calculating the recognition rate of each trajectory. Experiments demonstrate the superiority of our proposed HMM-based scheme using PCA coefficients of subtrajectories in comparison with other techniques in the literature.