Behavior Detection by Trajectory Analyzing Using Topic Modeling

In this paper, an unsupervised framework is presented to learn motion patterns by using hierarchical Bayesian models. It is also employed for activity analysis in visual surveillance. In this research, the concept of activities as motion patterns is considered as a correspondence to far-field camera view. Objects are tracked by using low-level features and then the location and speed of objects are computed as a feature along with trajectories. Under LDA probabilistic model, activities’ distributions are learned in feature space. Since there is not an analytic solution for these models, variational inference method is used to approximate latent parameters of the model. This approach is separately measured on the captured data of several cameras and acceptable results are obtained

Behavior Detection by Trajectory Analyzing Using Topic Modeling Keywords:

Visual Surveillance , Behavior Detection , Activity Analysis , Topic Modeling , LDA Model , Variational Inference