Traffic density estimation via a multi-level feature fusion network
Author:
Funder
Humanity and Social Science Foundation of Ministry of Education, China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03188-x.pdf
Reference33 articles.
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2. Toropov E, Gui L, Zhang S, et al (2015) Traffic flow from a low frame rate city camera, Presented at IEEE International Conference on Image Processing (ICIP). IEEE 2015: 3802–3806
3. Zhang T, Automation SO (2014) Modeling and Analysis of Vehicle Flow Detection Based on Video. Computer and Digital Engineering
4. Abdelwahab MA (2018) Fast approach for efficient vehicle counting. Electronics Letters. 20–22
5. Liu Y, Tian B, Chen S, Zhu F, Wang K (2013) A survey of vision-based vehicle detection and tracking techniques in ITS. IEEE International Conference on Vehicular Electronics and Safety. https://doi.org/10.1109/ICVES.2013.6619606
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1. DSNet: A vehicle density estimation network based on multi-scale sensing of vehicle density in video images;Expert Systems with Applications;2023-12
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