A feature extraction based trajectory segmentation approach based on multiple movement parameters

Analyzing the trajectories of movements among moving objects is of interest in many fields to understand the dynamics and behavior of those objects. In this paper, a cluster-centric trajectory segmentation approach is proposed to reveal and visualize segments of trajectories among moving objects. Characteristics such as position, direction, and speed of moving objects (called movement parameters) are considered for this purpose. First, profiles generated for different movement parameters are divided into several portions using sliding windows of different length. Next, changes with respect to each particular movement parameter profile in the sliding windows are extracted as features. Finally, by clustering the extracted features, subsequences of trajectories with similar movement characteristics are detected. Some cluster-validity indices were used to find the (near) optimal number of clusters. The performance of the proposed segmentation technique is evaluated through a trajectory clustering as well as a movement pattern detection case study over some real-word datasets.