Siam R-CNN: Visual Tracking by Re-Detection

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 tem...

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Vydáno v:Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 6577 - 6587
Hlavní autoři: Voigtlaender, Paul, Luiten, Jonathon, Torr, Philip H.S., Leibe, Bastian
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.01.2020
Témata:
Benchmark testing
Dynamic programming
Feature extraction
Head
Heuristic algorithms
Target tracking
ISSN:1063-6919
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Shrnutí: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.
ISSN:1063-6919
DOI:10.1109/CVPR42600.2020.00661