  For visual object tracking, it is difficult to realize an almighty online
tracker due to the huge variations of target appearance depending on an image
sequence. This paper proposes an online tracking method that adaptively
aggregates arbitrary multiple online trackers. The performance of the proposed
method is theoretically guaranteed to be comparable to that of the best tracker
for any image sequence, although the best expert is unknown during tracking.
The experimental study on the large variations of benchmark datasets and
aggregated trackers demonstrates that the proposed method can achieve
state-of-the-art performance. The code is available at
https://github.com/songheony/AAA-journal.
