Next Article in Journal
Cellulose Nanopaper Cross-Linked Amino Graphene/Polyaniline Sensors to Detect CO2 Gas at Room Temperature
Next Article in Special Issue
Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation
Previous Article in Journal
A Classification Method for Select Defects in Power Transformers Based on the Acoustic Signals
Previous Article in Special Issue
Real-Time Vehicle-Detection Method in Bird-View Unmanned-Aerial-Vehicle Imagery
Open AccessFeature PaperArticle

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison

Dipartimento di Informatica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5213; https://doi.org/10.3390/s19235213
Received: 17 October 2019 / Revised: 24 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
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. View Full-Text
Keywords: vehicular traffic flow detection; vehicular traffic flow classification; vehicular traffic congestion; deep learning; video classification; deep learning; benchmark vehicular traffic flow detection; vehicular traffic flow classification; vehicular traffic congestion; deep learning; video classification; deep learning; benchmark
Show Figures

Figure 1

MDPI and ACS Style

Impedovo, D.; Balducci, F.; Dentamaro, V.; Pirlo, G. Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison. Sensors 2019, 19, 5213. https://doi.org/10.3390/s19235213

AMA Style

Impedovo D, Balducci F, Dentamaro V, Pirlo G. Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison. Sensors. 2019; 19(23):5213. https://doi.org/10.3390/s19235213

Chicago/Turabian Style

Impedovo, Donato; Balducci, Fabrizio; Dentamaro, Vincenzo; Pirlo, Giuseppe. 2019. "Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison" Sensors 19, no. 23: 5213. https://doi.org/10.3390/s19235213

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop