ArticleSensors (Basel, Switzerland)2019
Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Vehicle speed measurement method using monocular cameras.Scientific reports · 2025Article
- A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection.Sensors (Basel, Switzerland) · 2024Article
- Deep Learning-Based Congestion Detection at Urban Intersections.Sensors (Basel, Switzerland) · 2021Article
- Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.
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Registered trials
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