Learning Semantic Scene Models by Trajectory Analysis

Abstract. In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from longterm observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributio...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Xiaogang Wang, Kinh Tieu, Eric Grimson
• Keywords: Computer Vision, ECCV 2006, Semantic Scene Models, Similarity Measures, Trajectories