On the public citation graph

[Retracted] Self‐Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3

https://doi.org/10.1155/2021/2889115
0 signed · 26 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Retraction Watch lists this work as retracted (export 2026-11-11; notice 2023-12-20, 10.1155/2023/9872756). OpenCitations Index CSV, figshare 24356626 v8 (2026-09-08) records 9 citing works; 2 have dates after the notice under the dataset’s date-resolution rule.

1 of the 16 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1088/1757-899x/983/1/012003 (notice 2021-11-09).

Dataset join, best-effort; this page does not check the publisher record. Retraction Watch data via Crossref Labs, used with credit; OpenCitations Index (CC0) and Meta (CC BY 4.0); dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/dac.3977openalex-snapshot
Comparative analysis and application of LBP face image recognition algorithms
inferredcites →https://doi.org/10.1007/978-3-319-46448-0_45openalex-snapshot
Learning to Track at 100 FPS with Deep Regression Networks
inferredcites →https://doi.org/10.1016/j.imavis.2019.04.004openalex-snapshot
Adaptive Lucas-Kanade tracking
inferredcites →https://doi.org/10.1088/1757-899x/983/1/012003openalex-snapshot
An Efficient Power Theft Detection Using Mean-Shift Clustering and Deep Learning in Smart Grid
inferredcites →https://doi.org/10.1109/cecnet.2012.6201691openalex-snapshot
Object tracking based on improved MeanShift and SIFT
inferredcites →https://doi.org/10.1109/cvpr.1994.323794openalex-snapshot
Good features to track
inferredcites →https://doi.org/10.1109/cvpr.2010.5539960openalex-snapshot
Visual object tracking using adaptive correlation filters
inferredcites →https://doi.org/10.1109/cvpr.2013.312openalex-snapshot
Online Object Tracking: A Benchmark
inferredcites →https://doi.org/10.1109/cvpr.2016.156openalex-snapshot
Staple: Complementary Learners for Real-Time Tracking
inferredcites →https://doi.org/10.1109/cvpr.2016.95openalex-snapshot
Object Detection from Video Tubelets with Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00508openalex-snapshot
A Twofold Siamese Network for Real-Time Object Tracking
inferredcites →https://doi.org/10.1109/cvprw.2016.59openalex-snapshot
Learning by Tracking: Siamese CNN for Robust Target Association
inferredcites →https://doi.org/10.1109/iccvw.2017.235openalex-snapshot
Recurrent Filter Learning for Visual Tracking
inferredcites →https://doi.org/10.1109/ncc.2017.8077044openalex-snapshot
Computationally efficient deep tracker: Guided MDNet
inferredcites →https://doi.org/10.18280/isi.250517openalex-snapshot
Object Detection Using Stacked YOLOv3
inferredcites →https://doi.org/10.3390/s21062108openalex-snapshot
A Hybrid Tracking System of Full-Body Motion Inside Crowds

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.compag.2022.107347openalex-snapshot
Y-BGD: Broiler counting based on multi-object tracking
inferred← citeshttps://doi.org/10.1109/vitecon58111.2023.10157717openalex-snapshot
Classification and Segmentation of Marine Related Remote Sensing Imagery Data Using Deep Learning
inferred← citeshttps://doi.org/10.1155/2021/4617242openalex-snapshot
The System Research and Implementation for Autorecognition of the Ship Draft via the UAV
inferred← citeshttps://doi.org/10.1155/2022/3048611openalex-snapshot
Extracting Vessel Speed Based on Machine Learning and Drone Images during Ship Traffic Flow Prediction
inferred← citeshttps://doi.org/10.1155/2022/5443237openalex-snapshot
Moving Target Detection Technology Based on UAV Vision
inferred← citeshttps://doi.org/10.1155/2022/7817309openalex-snapshot
A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method
inferred← citeshttps://doi.org/10.1155/2023/9872756openalex-snapshot
Retracted: Self-Correction Ship Tracking and Counting with Variable Time Window Based on YOLOv3
inferred← citeshttps://doi.org/10.1371/journal.pone.0321863openalex-snapshot
YOLO-HPSD: A high-precision ship target detection model based on YOLOv10
inferred← citeshttps://doi.org/10.3390/electronics13153089openalex-snapshot
Lightweight Water Surface Object Detection Network for Unmanned Surface Vehicles
inferred← citeshttps://doi.org/10.3390/s23156777openalex-snapshot
Every Vessel Counts: Neural Network Based Maritime Traffic Counting System
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.