Related references
Note: Only part of the references are listed.
Article
Computer Science, Artificial Intelligence
Lianghua Huang et al.
Summary: GOT-10k is a large tracking database that covers over 560 classes of moving objects and 87 motion patterns, providing a unified training and evaluation platform for tracker development. Additionally, the introduction of a one-shot protocol for tracker evaluation avoids biased results towards familiar objects and promotes generalization in tracker development.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Christoph Mayer et al.
Summary: Tracking objects with similar appearances to the target is a fundamental challenge in visual tracking. While many methods try to suppress distractors through stronger appearance models, this approach focuses on tracking the distractor objects in order to continue tracking the target effectively by using a learned association network and training strategy, achieving outstanding performance in experiments.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Bin Yu et al.
Summary: This paper introduces a novel single-object discriminative tracker DTT based on Transformer architecture, utilizing self- and encoder-decoder attention mechanisms for end-to-end discrimination and bounding box prediction, significantly improving efficiency and robustness. DTT achieves state-of-the-art performance in four popular benchmarks, running at over 50 FPS.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zhipeng Zhang et al.
Summary: Siamese tracking has made significant progress by introducing six novel matching operators and optimizing their combination using binary channel manipulation (BCM), resulting in favorable performance gains for the tracker AutoMatch on various benchmark datasets.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Zikun Zhou et al.
Summary: This paper proposes a method to track the salient local parts of the target to address targets with various appearance variations; through fine-grained saliency mining and saliency-association modeling, effective representations are learned, resulting in favorable tracking performance against state-of-the-art trackers.
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
(2021)
Article
Computer Science, Artificial Intelligence
Zhipeng Zhang et al.
Summary: The proposed framework utilizes soft spatial constraints to obtain an accurate global segmentation map and iteratively enhances features at different resolutions by using the predicted mask as feedback guidance. This framework sets a new state-of-the-art on the latest pixelwise tracking benchmark VOT2020, surpassing SiamMask by significant margins on different datasets. The code is available at https://github.com/JudasDie/SOTS.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Hamed Kiani Galoogahi et al.
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Martin Danelljan et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Article
Computer Science, Artificial Intelligence
Joao F. Henriques et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2015)
