A real-time traffic congestion detection system using on-line images

Lam, Chan‐Tong; Gao, Hanyang; Ng, Benjamin K.

doi:10.1109/icct.2017.8359891

Cited by 29 publications

(14 citation statements)

References 7 publications

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“…One way is to compare the similarity of frames. References [26], [27] used some low-level image features, such as LBP (local binary pattern), Harr-like features and middle-level image features (sparse encoding), to compare the similarity of two adjacent frames, then images with high similarity are identified as congestion. Another way [28] is similar to the vehicle density based methods using successive video frames, but the vehicle and road are detected by the sematic segmentation technique with CNN method, which is more efficiently than the method based on the frame subtraction.…”

Section: B Methods Using Several Adjacent Framesmentioning

confidence: 99%

Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China Using Ultra-Low Frame Rate Videos

Sun

Wang

Wang³

et al. 2020

IEEE Access

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Traffic congestion detection plays an important role for road management. However, it is difficult to automatically report traffic congestion when it occurs in large-scale road network. One of key challenges for rapidly and precisely identifying early congestion is huge variations in appearance caused by illumination, weather, camera settings and other traffic conditions. To address it, we proposed a trafficoriented model to classify congestion from large dataset of ultra-low frame rate video captured from traffic surveillance system. The proposed deeply supervised traffic congestion detector has two modules: attention proposal module and deeply supervised inception network. Specifically, within the shallow layers, the binary edge/corner density features are used in attention proposal module to generate the rang of interest (ROI) mask automatically. This strategy keeps the training process focusing on the congestion features without disturbances. Following the attention proposal module, a very deep structure based on the inception network was used together to effectively extract rich and discriminative features then detect traffic congestion. The approach was tested on a self-established dataset based on empirical data, which contains images captured from 14470 surveillance cameras for monitoring 5,215 km of freeway in Shaanxi province, China. The experimental results show that the accuracy of the proposed method could reach 95.77% considering various disturbances, conditions and other limitations, which is improved than unsupervised networks.

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Section: B Methods Using Several Adjacent Framesmentioning

confidence: 99%

Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China Using Ultra-Low Frame Rate Videos

Sun

Wang

Wang³

et al. 2020

IEEE Access

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show abstract

“…Haar features are extracted in the test phase and then compared with those used in the training phase, in order to obtain a positive or negative identification. Haar Cascade has been successfully used for Vehicle Detection [26], also to evaluate a traffic congestion index [27].…”

Section: Methodsmentioning

confidence: 99%

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

Impedovo

Balducci

Dentamaro

et al. 2019

Sensors

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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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“…Moreover, with the continuous improvement of traffic video surveillance systems, ITS based on surveillance video has gradually become a hotspot. In Lam et al [2], the correlation coefficient between consecutive frames and the number of vehicles obtained by background modeling was introduced for congestion detection. However, this method is suitable for slow or even static congestion detection, and the image correlation value is sensitive to the environment.…”

Section: Related Workmentioning

confidence: 99%

“…Among all kinds of traffic congestion detection methods, due to the advantages of no damage to the road surface and real-time collection of traffic information, the detection method based on surveillance video is gradually widely used in the intelligent transportation system (ITS). Currently, there are many traffic congestion detection methods [1][2][3] based on video surveillance. However, real-time performance and accuracy still cannot be traded off in current methods.…”

Section: Introductionmentioning

confidence: 99%

Whether and how congested is a road? Indices, updating strategy and a vision‐based detection framework

Gao

You

Wang

et al. 2023

IET Intelligent Trans Sys

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Traffic congestion detection method based on surveillance video is gradually widely used in intelligent transportation systems (ITS). Due to complex challenges such as weather change, vehicle occlusion, camera jitter, camera installation location, and so on, current methods are difficult to balance in real time and accuracy. Here, a new real‐time and robust traffic congestion detection framework and a vision‐based multi‐dimensional congestion detection model are proposed. Firstly, the framework introduces an object detector based on the lightweight convolutional neural network (CNN) and an efficient multi‐object IoU‐like tracker to obtain traffic dynamic information in real time. Then, traffic density, traffic velocity, and duration of instantaneous congestion are defined and a multi‐dimensional congestion detection model is established. Furthermore, an adaptive updating strategy of dynamic parameters is investigated. Finally, in multiple groups of comparative experiments, the framework is verified to be applicable to a variety of lightweight CNN detectors and IoU‐like trackers. The precision and recall of the multi‐dimensional congestion model can reach 95.1% and 92.1% respectively, with 43FPS. The comparative experimental results show that the proposed method is real‐time, more robust and accurate, and can be employed for online traffic congestion detection based on surveillance video.

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A real-time traffic congestion detection system using on-line images

Cited by 29 publications

References 7 publications

Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China Using Ultra-Low Frame Rate Videos

Exploiting Deeply Supervised Inception Networks for Automatically Detecting Traffic Congestion on Freeway in China Using Ultra-Low Frame Rate Videos

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

Whether and how congested is a road? Indices, updating strategy and a vision‐based detection framework

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