Data Collection and Deep Learning for Traffic and Road Infrastructure Management

Rajendran, S.; Jayavel, Kayalvizhi; Bharathi, N.

doi:10.1007/978-981-15-5329-5_38

Cited by 1 publication

(1 citation statement)

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“…After the rapid proliferation of deep learning based on deep neural networks, numerous studies leveraged this more efficient and accurate tool for traffic data collection. For example, DeepSORT and Lucas-Kanade were used for vehicle speed and counts extraction, and multi-layer perceptions were used for license plate information extraction (Rajendran et al, 2021). Huang et al (2020) proposed to using deep learning for detecting crash occurrence and predicting crash risk with roadside radar sensors, and Sreekumar et al (2017) developed a traffic pattern collection and analysis model, in which the detection and classification model was based on a YOLOv2 object detection model.…”

Section: Traffic Data Collection With Video Image Processingmentioning

confidence: 99%

Real-Time Intersection Vehicle Turning Movement Counts from Live UAV Video Stream Using Multiple Object Tracking

Wang,

Ho,

Wang

2023

J. Int. Con. Veh.

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The intelligent transportation system (ITS) is committed to ensuring safe and effective next-generation traffic throughout a city. However, such efficient operation on urban traffic networks needs the support of big traffic data, especially Turning Movement Counts (TMC) at intersections. Generally, TMC data are more challenging to collect due to labor cost and accuracy problems. In this paper, we leverage the capabilities of Unmanned Aerial Vehicles (UAV) to collect real-time TMC data in a cost-efficient way. We proposed a real-time TMC data collection framework based on a live video stream. The vehicle tracking capability is boosted by multiple object tracking based on tracking by detection. In addition, a challenging case study was conducted, and our results demonstrate the feasibility and robustness of the proposed TMC data collection framework. Specifically, with a GTX 1650 graphics card, about 10 FPS can be achieved in real-time for the TMC data collection. The overall accuracy is 91.93%, and the best case is over 98% accurate. In the context of miscounting, the major reason is due to ID switching caused by background occlusion. The proposed framework is expected to provide real-time data for traffic capacity analysis and advanced traffic simulation such as digital twins.

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Section: Traffic Data Collection With Video Image Processingmentioning

confidence: 99%