Improved Real-Time Traffic Congestion Detection with Automatic Image Cropping using Online Camera Images

Liu, Bowie; Lam, Chan‐Tong; Ng, Benjamin K.

doi:10.1109/icct52962.2021.9657853

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Although classification-based methods perform well in detecting traffic congestion, this approach only classifies images into two states: congested and uncongested, ignoring the various complex scenes that occur during traffic congestion. In addition, the literature [11,27,28] proposed real-time discrimination methods for urban road traffic congestion. He et al [11] used a road congestion index and network congestion index to, respectively, measure the degree of congestion on urban roads and road networks.…”

Section: Related Workmentioning

confidence: 99%

“…Lam et al [27] proposed the mIOU method, which detects traffic congestion by calculating the ratio of the overlap area between two images taken at a certain time interval and the union of multiple bounding boxes. Since the instantaneous mIOU proposed in the literature [27] performs poorly when the time interval is short, Liu et al [28] proposed a new weighted mIOU method. First, the boundary box generated by YOLOv4 is used to automatically crop the image to generate the region of interest, and then, the weighted average of the current and previous instantaneous mIOU values is taken to improve the application of the mIOU method in traffic congestion detection.…”

Section: Related Workmentioning

confidence: 99%

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An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios

et al. 2023

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Traffic congestion detection based on vehicle detection and tracking algorithms is one of the key technologies for intelligent transportation systems. However, in expressway surveillance scenarios, small vehicle size and vehicle occlusion present severe challenges for this method, including low vehicle detection accuracy and low traffic congestion detection accuracy. To address these challenges, this paper proposes an improved version of the CrowdDet algorithm by introducing the Involution operator and bi-directional feature pyramid network (BiFPN) module, which is called IBCDet. The proposed IBCDet module can achieve higher vehicle detection accuracy in expressway surveillance scenarios by enabling long-distance information interaction and multi-scale feature fusion. Additionally, a vehicle-tracking algorithm based on IBCDet is designed to calculate the running speed of vehicles, and it uses the average running speed to achieve traffic congestion detection according to the Chinese expressway level of serviceability (LoS) criteria. Adequate experiments are conducted on both the self-built Nanjing Raoyue expressway monitoring video dataset (NJRY) and the public dataset UA-DETRAC. The experimental results demonstrate that the proposed IBCDet outperforms the commonly used object detection algorithms in both vehicle detection accuracy and traffic congestion detection accuracy.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios

et al. 2023

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“…This framework is low-cost and off-the-shelf as it can be directly deployed on the system with surveillance cameras. Compared with our previous works on congestion detection [7]- [9], [11], we propose to consider multiple camera sites for congestion detection. Moreover, we consider traffic forecasting in this paper.…”

Section: Introductionmentioning

confidence: 99%

A Graph-Based Framework for Traffic Forecasting and Congestion Detection Using Online Images From Multiple Cameras

Liu,

Lam,

et al. 2024

IEEE Access

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This paper presents a low-cost graph-based traffic forecasting and congestion detection framework using online images from multiple cameras. The advantage of using a graph neural network (GNN) for traffic forecasting and detection is that it represents the traffic network in a natural way. This framework requires only images from surveillance cameras without any other sensors. It converts the online images into two types of data: traffic volume and image-based traffic occupancy. A clustering-based graph construction method is proposed to build a graph based on the traffic network. For traffic forecasting, multiple models, including statistical models and deep graph convolutional neural networks (GCNs), are used and compared using the extracted data. The framework uses logistic regression to determine the threshold of traffic congestion. In the experiment, we found that the Decoupled Dynamic Spatial-Temporal Graph Neural Network (D2STGNN) model achieved the best performance on the collected dataset. We also propose a threshold-based method for detecting traffic congestion using traffic volume and image-based traffic occupancy. This framework provides a low-cost solution for traffic forecasting and congestion detection when only surveillance images are available.

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Traffic Flow Velocity Estimation from Single Camera Data

Minh,

Thai,

Duc

et al. 2024

Communications in Computer and Information Science

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Improved Real-Time Traffic Congestion Detection with Automatic Image Cropping using Online Camera Images

Cited by 5 publications

References 8 publications

An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios

An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios

A Graph-Based Framework for Traffic Forecasting and Congestion Detection Using Online Images From Multiple Cameras

Traffic Flow Velocity Estimation from Single Camera Data

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