Unsupervised Tracking From Clustered Graph Patterns

Abstract—This paper shows how data mining and in partic-ular graph mining and clustering can help to tackle difficult tracking problems such as tracking possibly multiple objects in a video with a moving camera and without any contextual information on the objects to track. Starting from different segmentations of the video frames (dynamic and non dynamic ones), we extract frequent subgraph patterns to create spatio-temporal patterns that may correspond to interesting objects to track. We then cluster the obtained spatio-temporal patterns to get longer and more robust tracks along the video. We compare our tracking method called TRAP to two state-of-the-art tracking ones and show on three synthetic and real videos that our method is effective in this difficult context. I. INTRODUCTION AND RELATED WORK Many ongoing research works concerning object trackin