  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 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.
