An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos

Li, Jing; Chen, Shuo; Zhang, Fangbing; Li, Erkang; Yang, Tao; Lu, Zhaoyang

doi:10.3390/rs11101241

Cited by 35 publications

(59 citation statements)

References 36 publications

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“…For the UAVDT-Benchmark dataset (M0101), the altitude is described as higher than 70 m [47]. For the FHY-XD-UAV-DATA dataset (Scene 2 and Scene 5), the flight altitude is generally between 50 to 80 m [69]. No information about the title angles of these videos is available.…”

Section: Methodsmentioning

confidence: 99%

“…This metric was successfully applied to track humans through long periods of partial occlusions. Recently, Li et al [69] proposed a speed estimation method from traffic videos captured by UAVs. They first follow a tracking-by-detection framework for vehicle tracking and then conduct vehicle speed estimation.…”

Section: Related Workmentioning

confidence: 99%

“…While speed-estimation results were metrically evaluated, no quantitative measures were provided for detection and tracking. The tracking method of [69] has originally been applied for human tracking [61], which was shown to suffer from severe identity-switching issues. There are also traditional approaches based on computer vision techniques that are still popular [22,72,73].…”

Section: Related Workmentioning

confidence: 99%

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Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

2019

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Along with the advancement of light-weight sensing and processing technologies, unmanned aerial vehicles (UAVs) have recently become popular platforms for intelligent traffic monitoring and control. UAV-mounted cameras can capture traffic-flow videos from various perspectives providing a comprehensive insight into road conditions. To analyze the traffic flow from remotely captured videos, a reliable and accurate vehicle detection-and-tracking approach is required. In this paper, we propose a deep-learning framework for vehicle detection and tracking from UAV videos for monitoring traffic flow in complex road structures. This approach is designed to be invariant to significant orientation and scale variations in the videos. The detection procedure is performed by fine-tuning a state-of-the-art object detector, You Only Look Once (YOLOv3), using several custom-labeled traffic datasets. Vehicle tracking is conducted following a tracking-by-detection paradigm, where deep appearance features are used for vehicle re-identification, and Kalman filtering is used for motion estimation. The proposed methodology is tested on a variety of real videos collected by UAVs under various conditions, e.g., in late afternoons with long vehicle shadows, in dawn with vehicles lights being on, over roundabouts and interchange roads where vehicle directions change considerably, and from various viewpoints where vehicles’ appearance undergo substantial perspective distortions. The proposed tracking-by-detection approach performs efficiently at 11 frames per second on color videos of 2720p resolution. Experiments demonstrated that high detection accuracy could be achieved with an average F1-score of 92.1%. Besides, the tracking technique performs accurately, with an average multiple-object tracking accuracy (MOTA) of 81.3%. The proposed approach also addressed the shortcomings of the state-of-the-art in multi-object tracking regarding frequent identity switching, resulting in a total of only one identity switch over every 305 tracked vehicles.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

2019

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“…YOLOv2, which is a representative end-to-end object-detection algorithm based on CNN, was applied to vehicle detection in a single image [27]. Li et al [28] estimated the ground speed of multiple vehicles based on a traffic dataset by unmanned aerial vehicles (UAVs) through YOLOv3 for object detection and motion compensation. In addition, Wang et el.…”

Section: Introductionmentioning

confidence: 99%

Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes using a Convolutional Neural Network

Seong¹,

Song²,

Yoon³

et al. 2019

Sensors

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In this manuscript, a new method for the determination of vehicle trajectories using an optimal bounding box for the vehicle is developed. The vehicle trajectory is extracted using images acquired from a camera installed at an intersection based on a convolutional neural network (CNN). First, real-time vehicle object detection is performed using the YOLOv2 model, which is one of the most representative object detection algorithms based on CNN. To overcome the inaccuracy of the vehicle location extracted by YOLOv2, the trajectory was calibrated using a vehicle tracking algorithm such as a Kalman filter and intersection-over-union (IOU) tracker. In particular, we attempted to correct the vehicle trajectory by extracting the center position based on the geometric characteristics of a moving vehicle according to the bounding box. The quantitative and qualitative evaluations indicate that the proposed algorithm can detect the trajectories of moving vehicles better than the conventional algorithm. Although the center points of the bounding boxes obtained using the existing conventional algorithm are often outside of the vehicle due to the geometric displacement of the camera, the proposed technique can minimize positional errors and extract the optimal bounding box to determine the vehicle location.

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“…The structure of the longitudinal section is shown in Figure 3. The point cloud of the steel arch areas (the red points in Figure 3) has unique characteristics, which are different from other kinds of feature lines, such as the road edge [5,6], building outline [7,8], and components edge [9]. The most significant geometric feature of steel arches is that it is a standard I-steel with a smooth surface and a regular arch corner.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

Zhang

Qiu

Song

et al. 2019

Sensors

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Automation is an inevitable trend in the development of tunnel shotcrete machinery. Tunnel environmental perception based on 3D LiDAR point cloud has become a research hotspot. Current researches about the detection of tunnel point clouds focus on the completed tunnel with a smooth surface. However, few people have researched the automatic detection method for steel arches installed on a complex rock surface. This paper presents a novel algorithm to extract tunnel steel arches. Firstly, we propose a refined function for calibrating the tunnel axis by minimizing the density variance of the projected point cloud. Secondly, we segment the rock surface from the tunnel point cloud by using the region-growing method with the parameters obtained by analyzing the tunnel section sequence. Finally, a Directed Edge Growing (DEG) method is proposed to detect steel arches on the rock surface in the tunnel. Our experiment in the highway tunnels under construction in Changsha (China) shows that the proposed algorithm can effectively extract the points of the edge of steel arches from 3D LiDAR point cloud of the tunnel without manual assistance. The results demonstrated that the proposed algorithm achieved 92.1% of precision, 89.1% of recall, and 90.6% of the F-score.

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An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos

Cited by 35 publications

References 36 publications

Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos

Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes using a Convolutional Neural Network

Automatic Tunnel Steel Arches Extraction Algorithm Based on 3D LiDAR Point Cloud

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