AAA: Adaptive Aggregation of Arbitrary Online Trackers with Theoretical Performance Guarantee
This addresses the problem of handling appearance variations in object tracking for computer vision applications, offering a robust solution with proven performance.
The paper tackles the challenge of visual object tracking by proposing an adaptive aggregation method for multiple online trackers, achieving state-of-the-art performance on benchmark datasets with theoretical guarantees comparable to the best tracker.
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.