Description and Recognition of Activity Patterns Using Sparse Vector Fields

摘要

Far-field activities represented as time series or trajectories can be summarized in compact representations of frequent patterns. Popular representations such as clustering or probabilistic modeling of trajectories often do not inform about both velocity and direction of motion, which are by definition visually and quantitatively embedded in vector fields. However, a common use of vector fields may dismiss information about forbidden areas, or regions with concurrent activity patterns. To address this problem we present a non-iterative layered vector field estimation process that yields sparse vector field abstractions of activity patterns from groups of trajectories. The key feature of our approach is the estimate of the probability density function (PDF) of targets positions: it automatically tunes the cost function parameter, and serves as weights in the sparse estimation problem. We also propose a trajectory labeling algorithm that labels trajectories according to their activity patterns using the vector field abstractions. Experiments in synthetic and real trajectory data show that the proposed estimation approach yields correctly sparse vector fields, which are similar to known generating vector fields, and 5-12% higher labeling accuracy on test trajectories when compared to other generative models. Outlier trajectories are also detected.