Towards More Accurate Tiny Object Tracking: Benchmark and Algorithm-Reference-Cited by-同舟云学术

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Towards More Accurate Tiny Object Tracking: Benchmark and Algorithm

Published:2024-05-08 Issue: Volume: Page:1858-1863
ISSN:
Container-title:2024 27th International Conference on Computer Supported Cooperative Work in Design (CSCWD)
language:
Short-container-title:

Author:

Yu Jizhe¹,Liu Yu¹,Wei Hongkui²,Xu Kaiping¹

Affiliation:

1. Dalian University of Technology,DUT School of Software Technology & DUT-RU International School of Information Science and Engineering,Dalian,China,116024

2. Beijing Institute of Electronic System Engineering,State Key Laboratory of Intelligent Manufacturing System Technology,Beijing,China,100854

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10579968/10579982/10580827.pdf?arnumber=10580827

Reference21 articles.

1. TrackNet: A Deep Learning Network for Tracking High-speed and Tiny Objects in Sports Applications*

2. TrackNetV2: Efficient Shuttlecock Tracking Network

3. Very deep convolutional networks for large-scale image recognition[J];Simonyan,2014

4. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

5. Fully convolutional networks for semantic segmentation

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