The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Automatic object tracking in optimized UAV video
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“[24] Faster R-CNN | VGG-16 | i. UAV123 ii. VIVID | i. 1280 × 720 ii. 640 × 480 | i. 5 to 25m ii. High altitude | Different views | One class (Car) | i. 96.03 ii. 96.19 | -- | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [24]
In the citing paper
“[24] | To track the object automatically in optimized UAV video. | Tracking-by-detection | KF | 96.03 | -- | - |”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [24]
In the citing paper
“[24] Faster R-CNN | VGG-16 | i. UAV123 ii. VIVID | i. 1280 × 720 ii. 640 × 480 | i. 5 to 25m ii. High altitude | Different views | One class (Car) | i. 96.03 ii. 96.19 | -- | -- | --”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [24]
Citations
Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
An Introduction to the Kalman Filter
Welch, G., & Bishop, G. (1995) · CiteSeer X (The Pennsylvania State University) · Cited by 4,895
A Benchmark and Simulator for UAV Tracking
Mueller, M., Smith, N., & Ghanem, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_27 · Cited by 1,944
Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Chen, X., Xiang, S., Liu, C.-L., & Pan, C.-H. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2309695 · Cited by 622
Satellite Based Mapping of Ground PM2.5 Concentration Using Generalized Additive Modeling
Zou, B., Chen, J., Zhai, L., Fang, X., & Zheng, Z. (2016) · Remote Sensing · doi:10.3390/rs9010001 · Cited by 450
Deep Learning Approach for Car Detection in UAV Imagery
Ammour, N., Alhichri, H. S., Bazi, Y., Benjdira, B., Alajlan, N., & Zuair, M. (2017) · Remote Sensing · doi:10.3390/rs9040312 · Cited by 301
An Enhanced Viola-Jones Vehicle Detection Method From Unmanned Aerial Vehicles Imagery
Xu, Y.-Z., Yu, G., Wu, X.-K., Wang, Y.-P., & Ma, Y.-L. (2017) · IEEE transactions on intelligent transportation systems (Print) · doi:10.1109/tits.2016.2617202 · Cited by 94
Application of Object Detection and Tracking Techniques for Unmanned Aerial Vehicles
Kamate, S., & Yilmazer, N. (2015) · Procedia Computer Science · doi:10.1016/j.procs.2015.09.183 · Cited by 92
Kalantar, B., Mansor, S. B., Halin, A. B. A., Shafri, H. Z. M., & Zand, M. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2703621 · Cited by 64
Research works printed but not linked: 2
- Yu W, Yang K, Bai Y, Xiao T, Yao H, Rui Y (2016) Visualizing and comparing AlexNet and VGG using deconvolutional layers. In: 33rd International Conference on Machine Learning, vol 48
- Collins RT, Zhou X, Teh SK (2005) An open source tracking testbed and evaluation web site. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance
Cited by
A Systematic Review of Drone Based Road Traffic Monitoring System
Bisio, I., Garibotto, C., Haleem, H., Lavagetto, F., & Sciarrone, A. (2022) · IEEE Access · doi:10.1109/access.2022.3207282 · Cited by 139
“KF technique is implemented in [24] to track the vehicles in a UAV sequence, where vehicle’s current state is predicted based upon the motion model of the last state and then vehicle’s position is updated by leveraging its cen-979 troid information.”
MethodResult
Metadata
- Authors
- Micheal, A. A., & Vani, K.
- Affiliations
- Anna University, Chennai (IN)
- Corresponding author
- Micheal, Ancy A.
- Venue
- The Journal of Supercomputing
- Published
- 2019-02-15
- Volume
- 75
- Issue
- 8
- Pages
- 4986-4999
- ISSN
- 0920-8542 (ISSN-L)
- 1573-0484
- OpenAlex
- W2913048641
- Cited by
- 7
- References
- 16
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 1 · 2022: 2 · 2021: 2
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The Journal of Supercomputing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- object tracking, object detection, surveillance, Faster R-CNN, R-CNN, Kalman filter, car detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
