Siam R-CNN: Visual Tracking by Re-Detection [article]

Paul Voigtlaender, Jonathon Luiten, Philip H.S. Torr, Bastian Leibe
2020 arXiv   pre-print
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked and potential distractor objects. This enables our approach to make better tracking decisions, as well
more » ... as to re-detect tracked objects after long occlusion. Finally, we propose a novel hard example mining strategy to improve Siam R-CNN's robustness to similar looking objects. Siam R-CNN achieves the current best performance on ten tracking benchmarks, with especially strong results for long-term tracking. We make our code and models available at www.vision.rwth-aachen.de/page/siamrcnn.
arXiv:1911.12836v2 fatcat:alfpbtovnnaergxyha77o3qz2u