Abstract
This paper presents a new video representation that exploits the geometric relationships among trajectories for human action recognition. Geometric relationships are provided by applying the Delaunay triangulation method on the trajectories of each video frame. Then, graph encoding method called bag of graphs (BOG) is proposed to handle the geometrical relationships between trajectories. BOG considers local graph descriptors to learn a more discriminative graph-based codebook and to represent the video with a histogram of visual graphs. The graphbased codebook is composed of the centers of graph clusters. To define graph clusters, a classification graph technique based on the Hungarian distance is proposed. Experiments using the human action recognition datasets (Hollywood2 and UCF50) show the effectiveness of the proposed approach.