CAROM - Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures

Lü, Dan; Jammula, Varun Chandra; Como, Steven; Wishart, Jeffrey; Chen, Yan; Yang, Yezhou

doi:10.1109/icra48506.2021.9561190

Cited by 20 publications

(15 citation statements)

References 31 publications

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“…This result is better than the previously reported results by Lu et al [ 21 ], which was described in Section 2 . Their results showed that the average localization error was 1.81 m using a differential GPS as a reference and 1.68 m using a drone as a reference.…”

Section: Validation Of Vehicle Localization Error Correctioncontrasting

confidence: 83%

“…In the table, it is clearly seen that the errors corrected by the linear regression model are much smaller than those without any correction with an average improvement of 71.32%. This result is better than the previously reported results by Lu et al [21], which was described in Section 2. Their results showed that the average localization error was 1.81 m using a differential GPS as a reference and 1.68 m using a drone as a reference.…”

Section: Vehicle Localization In Dynamic Conditioncontrasting

confidence: 79%

“…However, the use of the complex system requires high computational power, which may limit its application in certain scenarios. Another study used a combination of stereo vision and deep learning to reconstruct accurate vehicle locations in a traffic scene [ 21 ]. The system utilizes multiple cameras to capture a video of a traffic scene, then the video frames are processed by a deep neural network to construct 3D bounding boxes around the detected vehicles.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Yoon

2023

Sensors

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Optimizing traffic control systems at traffic intersections can reduce the network-wide fuel consumption, as well as emissions of conventional fuel-powered vehicles. While traffic signals have been controlled based on predetermined schedules, various adaptive signal control systems have recently been developed using advanced sensors such as cameras, radars, and LiDARs. Among these sensors, cameras can provide a cost-effective way to determine the number, location, type, and speed of the vehicles for better-informed decision-making at traffic intersections. In this research, a new approach for accurately determining vehicle locations near traffic intersections using a single camera is presented. For that purpose, a well-known object detection algorithm called YOLO is used to determine vehicle locations in video images captured by a traffic camera. YOLO draws a bounding box around each detected vehicle, and the vehicle location in the image coordinates is converted to the world coordinates using camera calibration data. During this process, a significant error between the center of a vehicle’s bounding box and the real center of the vehicle in the world coordinates is generated due to the angled view of the vehicles by a camera installed on a traffic light pole. As a means of mitigating this vehicle localization error, two different types of regression models are trained and applied to the centers of the bounding boxes of the camera-detected vehicles. The accuracy of the proposed approach is validated using both static camera images and live-streamed traffic video. Based on the improved vehicle localization, it is expected that more accurate traffic signal control can be made to improve the overall network-wide energy efficiency and traffic flow at traffic intersections.

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Section: Validation Of Vehicle Localization Error Correctioncontrasting

confidence: 83%

Section: Vehicle Localization In Dynamic Conditioncontrasting

confidence: 79%

Section: Related Workmentioning

confidence: 99%

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Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Yoon

2023

Sensors

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“…Currently, there exist few publications on vehicle detection and tracking using roadside sensors for traffic-monitoring applications. One such method, presented in [ 21 ], employs a sensor fusion algorithm that combines data from multiple sensors using an extended Kalman filter algorithm, resulting in a lateral error of 0.53 m, longitudinal error of 1.19 m, and combined Euclidean error of 1.43 m. Another method, proposed in [ 36 ], uses multiple cameras to construct 3D bounding boxes around vehicles to determine the vehicles’ locations. The results showed an average localization error of 1.81 m in the Euclidean distance using a differential GPS as a reference and 1.68 m in the Euclidean distance using a drone as a reference.…”

Section: Resultsmentioning

confidence: 99%

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

Yoon

2023

Sensors

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Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

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“…Most roadside monocular localization methods [16], [31] rely on manually selecting at least four corresponding points between the image and the map, solving for the homography matrix, and achieving the conversion from pixel coordinates to world coordinates. However, this manual point selection process may introduce localization errors in practical scenarios.…”

Section: Spatial Synchronizationmentioning

confidence: 99%

A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems

Li,

Zhao,

Chen

et al. 2023

Preprint

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<p>Spatial synchronization in roadside scenarios is essential for integrating data from multiple sensors at different locations. Current methods using cascading spatial transformation (CST) often lead to cumulative errors in large-scale deployments. Manual camera calibration is insufficient and requires extensive manual work, and existing methods are limited to controlled or single-view scenarios. To address these challenges, our research introduces a parallel spatial transformation (PST)-based framework for large-scale, multi-view, multi-sensor scenarios. PST parallelizes sensor coordinate system transformation, reducing cumulative errors. We incorporate deep learning for precise roadside monocular global localization, reducing manual work. Additionally, we use geolocation cues and an optimization algorithm for improved synchronization accuracy. Our framework has been tested in real-world scenarios, outperforming CST-based methods. It significantly enhances large-scale roadside multi-perspective, multi-sensor spatial synchronization, reducing deployment costs.</p>

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CAROM - Vehicle Localization and Traffic Scene Reconstruction from Monocular Cameras on Road Infrastructures

Cited by 20 publications

References 31 publications

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Vehicle Localization in 3D World Coordinates Using Single Camera at Traffic Intersection

Sensor Fusion-Based Vehicle Detection and Tracking Using a Single Camera and Radar at a Traffic Intersection

A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems

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