Traffic flow estimation with data from a video surveillance camera

This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the...

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Veröffentlicht in:Journal of big data Jg. 6; H. 1; S. 1 - 15
Hauptverfasser: Fedorov, Aleksandr, Nikolskaia, Kseniia, Ivanov, Sergey, Shepelev, Vladimir, Minbaleev, Alexey
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 07.08.2019
Springer Nature B.V
SpringerOpen
Schlagworte:
Algorithms
Anchors
Big Data
Cameras
Classification
Communications Engineering
Computational Science and Engineering
Computer Science
Convolutional neural network
Counting
Data
Data Mining and Knowledge Discovery
Database Management
Driving
Electronic surveillance
Estimation
Heuristic
Heuristic methods
Information Storage and Retrieval
Mathematical Applications in Computer Science
Methodology
Networks
Optimization
Surveillance
Surveillance data
Traffic
Traffic analysis
Traffic flow
Traffic flow estimation
Traffic surveillance
Vehicle detection
Vehicles
Video data
Vehicle detection
Traffic analysis
Traffic flow estimation
Convolutional neural network
Surveillance data
ISSN:2196-1115, 2196-1115
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This study addresses the problem of traffic flow estimation based on the data from a video surveillance camera. Target problem here is formulated as counting and classifying vehicles by their driving direction. This subject area is in early development, and the focus of this work is only one of the busiest crossroads in city Chelyabinsk, Russia. To solve the posed problem, we employed the state-of-the-art Faster R-CNN two-stage detector together with SORT tracker. A simple regions-based heuristic algorithm was used to classify vehicles movement direction. The baseline performance of the Faster R-CNN was enhanced by several modifications: focal loss, adaptive feature pooling, additional mask branch, and anchors optimization. To train and evaluate detector, we gathered 982 video frames with more than 60,000 objects presented in various conditions. The experimental results show that the proposed system can count vehicles and classify their driving direction during weekday rush hours with mean absolute percentage error that is less than 10%. The dataset presented here might be further used by other researches as a challenging test or additional training data.
Bibliographie:ObjectType-Article-1
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ISSN:2196-1115
2196-1115
DOI:10.1186/s40537-019-0234-z