Region-of-Interest Tracking Based on Keypoint Trajectories on a Group of Pictures

This paper deals with region-of-interest (ROI) tracking for applications such as video surveillance or cinematographic post-production. An ROI is typically delineated by a bounding box or a basic shape such as an ellipse in the first frame of the video. The tracking problem consists in detecting the ROI throughout the video as it moves and deforms. This detection can be done based on the full content of the ROI. However, since the ROI is, by definition, an approximate segmentation of the actual object of interest, it includes some background. This can make the ROI detection less accurate and induce a drift. Instead, we propose to use keypoint extractors and local descriptors combined with robust motion estimation. The motion estimation relies on the analysis of the temporal trajectories, or tracks, of the keypoints in groups of pictures (GOP). Some results are presented on natural sequences. The proposed method seems accurate.