Real-time visual detection and tracking system for traffic monitoring

Fernández-Sanjurjo, Mauro; Bosquet, Brais; Mucientes, Manuel; Brea, V.M.

doi:10.1016/j.engappai.2019.07.005

Cited by 52 publications

(14 citation statements)

References 43 publications

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“…Further investigation on the training procedure and selection of specific parameters affecting the detection performance was provided by [97] concerning the YOLO framework [108]. Furthermore, to address the real-time monitoring and occlusion problems in traffic monitoring system, [109] used FPN [110] and observed high precision at the cost of high computational complexity when considering high resolution images. Focus was given on vehicle tracking to ensure real-time monitoring and to overcome the occlusion problem.…”

Section: ) Video-based Detectionmentioning

confidence: 99%

“…However, if a DL model takes a long time to detect the objects, this approach will likely not satisfy realtime constraints. Therefore, [109] used a different strategy in which detection was performed a predefined number of times, taking into account a real-time scenario. Furthermore, in [68], instance based tracking was used, where vehicles were detected in subsequent frames based on parameters collected from the instance segmentation.…”

Section: A: Tracking-by-detectionmentioning

confidence: 99%

“…From a different perspective, in [94] vehicle tracking is achieved by performing vehicle Re-IDentification (Re-ID) with deep features and motion estimation with KF technique. As stated in [109], DCF tracker alone is not only unable to handle the occlusion problem, but it also can not provide robust tracking. Therefore, a combined model of KF and DCF is used in [109], which is more robust and reliable than individual DCF tracker.…”

Section: B: Online and Filtering Based Trackingmentioning

confidence: 99%

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A Systematic Review of Drone Based Road Traffic Monitoring System

et al. 2022

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Drone deployment has become crucial in a variety of applications, including solutions to traffic issues in metropolitan areas and highways. On the other hand, data collected via drones suffers from several problems, including a wide range of object scales, angle variations, truncation, and occlusion. To process and manipulate visual data from the drones, a variety of image processing algorithms have been employed, each with a distinct aim. Additionally, recent breakthroughs in the field of Artificial Intelligence, particularly deep learning, have attracted broad interest and are being applied to many domains in the framework of smart cities, including road traffic monitoring. The purpose of this study is to conduct a systematic review of drone-based traffic monitoring systems from a deep learning perspective. This work focuses on vehicle detection, tracking, and counting, since they are fundamental building blocks towards founding solutions for traffic congestion, flow rate and vehicle speed estimation. Additionally, drone-based datasets are examined, which face issues and problems caused by the diversity of features inherent of drone devices. The review analysis presented in this work summarizes the literature solutions provided and deployed so far and discusses future research trends in establishing a comprehensive traffic monitoring system in support of the development of smart cities INDEX TERMS deep learning, drone, smart city, traffic monitoring, uav.

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Section: ) Video-based Detectionmentioning

confidence: 99%

Section: A: Tracking-by-detectionmentioning

confidence: 99%

Section: B: Online and Filtering Based Trackingmentioning

confidence: 99%

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A Systematic Review of Drone Based Road Traffic Monitoring System

et al. 2022

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“…For instance, Aslani et al [20] applied the Optical Flow algorithm to detect and track moving objects by the intensity changes of frames. To improve the MOT performance, Fernandez-Sanjurjo et al [42] built a real-time traffic monitoring system and data association with the Hungarian algorithm. Based on cameras equipped on an unmanned aerial vehicle (UAV), researchers [11,43] applied correlation filters [44] to MOT tasks.…”

Section: Traffic Object Trackingmentioning

confidence: 99%

Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR

Bai¹,

Nayak²,

Zhao³

et al. 2022

Preprint

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Endowed with advanced wireless communication and control technologies, Connected and Automated Vehicles (CAVs) are regarded as the revolutionary promoter for Cooperative Driving Automation (CDA) to improve system operational performance. Enabling of CDA requires high-fidelity and real-time information on surrounding environment of CAVs, which is available from onboard sensors (e.g., LiDAR, camera, radar) or vehicle-to-everything (V2X) communications. Nevertheless, the accessibility of this information may suffer from the range and occlusion of perception or limited penetration rates in connectivity. In this paper, to explore the potential of roadside sensors for CDA applications in a mixed traffic environment, we introduce the prototype of Cyber Mobility Mirror (CMM), a next-generation real-time traffic surveillance system for 3D object detection, classification, tracking, and reconstruction, to provide CAVs with wide-range high-fidelity perception information in a mixed traffic environment. The CMM system consists of six main components: 1) the data preprocessor to retrieve and pre-process raw data from the roadside LiDAR; 2) the 3D object detector to generate 3D bounding boxes based on point cloud data; 3) the multi-objects tracker to endow unique IDs to detected objects and estimate their dynamic states; 4) the global locator to map positioning information from the LiDAR coordinate to geographic coordinate using coordinate transformation; 5) the cloud-based communicator to transmit perception information from roadside sensors to equipped vehicles; and 6) the onboard advisor to reconstruct and display the real-time traffic conditions via Graphical User Interface (GUI). In this study, a field-operational prototype system is deployed at a real-world intersection, University Avenue and Iowa Avenue in Riverside, California to assess the feasibility and performance of our CMM system. Results from field tests demonstrate that our CMM prototype system can provide satisfactory perception performance with 96.99% precision and 83.62% recall. High-fidelity real-time traffic conditions (at the object level) can be displayed on the GUI of the equipped vehicle with a frequency of 3-4 Hz.

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“…Hence, this paper proposes an improved YOLV3 algorithm to help minimize the problems associated with small traffic signs and increase the overall YOLV3 performance. Furthermore, motivated from MOT (Multi-Object Tracking) [41], which is widely used to mark and track vehicles and pedestrians in videos in traffic surveillance systems and noisy crowd scenes most recently [42,43]. Deep-Sort (Simple Online and Real-time Tracking) [44] is adopted to overcome a series of adverse factors brought by camera motion to real-time video detection.…”

Section: Introductionmentioning

confidence: 99%

A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

Song

Huang

et al. 2021

Applied Sciences

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It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively.

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Real-time visual detection and tracking system for traffic monitoring

Cited by 52 publications

References 43 publications

A Systematic Review of Drone Based Road Traffic Monitoring System

A Systematic Review of Drone Based Road Traffic Monitoring System

Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR

A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

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