On-line fusion of trackers for single-object tracking

•The work focuses on the design of good strategies for the on-line fusion of trackers.•Fusion can operate at two levels: tracker output selection and model correction.•We show experimentally that the ability to predict drift is essential for fusion.•We propose a tracker complementarity measure to choose the best tracker combination. Visual object tracking is a fundamental function of computer vision that has been the object of numerous studies. The diversity of the proposed approaches leads to the idea of trying to fuse them and take advantage of their individual strengths while controlling the noise they may introduce in some circumstances. The work presented here describes a generic framework for combining and/or selecting on-line the different components of the processing chain of a set of trackers, and examines the impact of various fusion strategies. The results are assessed from a repertoire of 9 state-of-the-art trackers evaluated over 46 fusion strategies on the VOT 2013, VOT 2015 and OTB-100 datasets. A complementarity measure able to predict the overall performance of a given set of trackers is also proposed.