The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dengan memanfaatkan pembelajaran mendalam dan pembelajaran pengukuhan, ADNet (Rangkaian Keputusan Tindakan) mengatasi pendekatan lain. Walau bagaimanapun, kelajuan dan prestasinya masih dihadkan oleh faktor seperti anggaran skor keyakinan yang tidak boleh dipercayai dan tindakan sejarah yang berlebihan. Untuk menangani had di atas, pendekatan yang lebih pantas dan tepat yang dinamakan Faster-ADNet dicadangkan dalam kertas ini. Dengan mengoptimumkan proses penjejakan melalui rangkaian pengenalan semula status, pendekatan yang dicadangkan adalah lebih cekap dan 6 kali lebih pantas daripada ADNet. Pada masa yang sama, ketepatan dan kestabilan dipertingkatkan dengan penyingkiran tindakan sejarah. Eksperimen menunjukkan kelebihan Faster-ADNet.
Tiansa ZHANG
Beijing Institute of Technology,Chinese Academy of Sciences
Chunlei HUO
Chinese Academy of Sciences
Zhiqiang ZHOU
Beijing Institute of Technology
Bo WANG
Beijing Institute of Technology
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Salinan
Tiansa ZHANG, Chunlei HUO, Zhiqiang ZHOU, Bo WANG, "Faster-ADNet for Visual Tracking" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 3, pp. 684-687, March 2019, doi: 10.1587/transinf.2018EDL8214.
Abstract: By taking advantages of deep learning and reinforcement learning, ADNet (Action Decision Network) outperforms other approaches. However, its speed and performance are still limited by factors such as unreliable confidence score estimation and redundant historical actions. To address the above limitations, a faster and more accurate approach named Faster-ADNet is proposed in this paper. By optimizing the tracking process via a status re-identification network, the proposed approach is more efficient and 6 times faster than ADNet. At the same time, the accuracy and stability are enhanced by historical actions removal. Experiments demonstrate the advantages of Faster-ADNet.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8214/_p
Salinan
@ARTICLE{e102-d_3_684,
author={Tiansa ZHANG, Chunlei HUO, Zhiqiang ZHOU, Bo WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Faster-ADNet for Visual Tracking},
year={2019},
volume={E102-D},
number={3},
pages={684-687},
abstract={By taking advantages of deep learning and reinforcement learning, ADNet (Action Decision Network) outperforms other approaches. However, its speed and performance are still limited by factors such as unreliable confidence score estimation and redundant historical actions. To address the above limitations, a faster and more accurate approach named Faster-ADNet is proposed in this paper. By optimizing the tracking process via a status re-identification network, the proposed approach is more efficient and 6 times faster than ADNet. At the same time, the accuracy and stability are enhanced by historical actions removal. Experiments demonstrate the advantages of Faster-ADNet.},
keywords={},
doi={10.1587/transinf.2018EDL8214},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - Faster-ADNet for Visual Tracking
T2 - IEICE TRANSACTIONS on Information
SP - 684
EP - 687
AU - Tiansa ZHANG
AU - Chunlei HUO
AU - Zhiqiang ZHOU
AU - Bo WANG
PY - 2019
DO - 10.1587/transinf.2018EDL8214
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2019
AB - By taking advantages of deep learning and reinforcement learning, ADNet (Action Decision Network) outperforms other approaches. However, its speed and performance are still limited by factors such as unreliable confidence score estimation and redundant historical actions. To address the above limitations, a faster and more accurate approach named Faster-ADNet is proposed in this paper. By optimizing the tracking process via a status re-identification network, the proposed approach is more efficient and 6 times faster than ADNet. At the same time, the accuracy and stability are enhanced by historical actions removal. Experiments demonstrate the advantages of Faster-ADNet.
ER -