An Accurate Multi-sensor Multi-target Localization Method for Cooperating Vehicles

Goli, Sepideh Afkhami; Far, Behrouz H.; Fapojuwo, Abraham O.

doi:10.1007/978-3-319-31311-5_9

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Additionally, in realistic traffic conditions, as the increase in the number and size of obstacles would limit the effective communication range [22]. In our study, as a typical effective detecting range in the urban area is around 80 m, a situation may exist that some original valid localisation vehicles would drive out of the effective detecting range, while some rest surrounding vehicles would move into the effective detecting range.…”

Section: Simulation Results and Discussionmentioning

confidence: 95%

“…Thus, an assumption is hold, for which we do not consider some special external environments, such as building blockings and tunnels, because it may produce abnormal discontinuous readings from the GPS. As the GPS noise mainly comes from two factors, one is the relative geometry of visible satellites, while the other one is the error for computing the pseudo‐range distance between the GPS receiver and the satellite position, generally, a zero‐mean Gaussian noise can be added to the real position to simulate the data from the GPS sensor [19, 22]. Another assumption in our simulation is that the whole fusion framework is on the basis of VANET and the communication quality among vehicles is great.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Song

Ling

Cao

et al. 2018

IET Intelligent Transport Systems

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Section: Simulation Results and Discussionmentioning

confidence: 95%

Section: Simulation Results and Discussionmentioning

confidence: 99%

Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Song

Ling

Cao

et al. 2018

IET Intelligent Transport Systems

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“…Most of the existing MOT methods rely on motion information produced from Kalman Filter [21], Hungarian algorithm with Kalman Filter [17], Particle Filter [22], or probability hypothesis density filter [23]. However, in autonomous driving systems, due to the uncertainty of the scene, it is impossible to track objects stably only by using motion information.…”

Section: Multiobject Trackingmentioning

confidence: 99%

Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information

et al. 2020

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Multiobject Tracking (MOT) is one of the most important abilities of autonomous driving systems. However, most of the existing MOT methods only use a single sensor, such as a camera, which has the problem of insufficient reliability. In this paper, we propose a novel Multiobject Tracking method by fusing deep appearance features and motion information of objects. In this method, the locations of objects are first determined based on a 2D object detector and a 3D object detector. We use the Nonmaximum Suppression (NMS) algorithm to combine the detection results of the two detectors to ensure the detection accuracy in complex scenes. After that, we use Convolutional Neural Network (CNN) to learn the deep appearance features of objects and employ Kalman Filter to obtain the motion information of objects. Finally, the MOT task is achieved by associating the motion information and deep appearance features. A successful match indicates that the object was tracked successfully. A set of experiments on the KITTI Tracking Benchmark shows that the proposed MOT method can effectively perform the MOT task. The Multiobject Tracking Accuracy (MOTA) is up to 76.40% and the Multiobject Tracking Precision (MOTP) is up to 83.50%.

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“…Traditional multi-object tracking approaches rely only on motion indications; e.g., multiple hypothesis tracking [58], joint probabilistic data association filter [59], Kalman filter [60], Hungarian algorithm with Kalman filter [61], and probability hypothesis density filter [62]. The complexity of these filters increases considerably as the number of tracked objects increases.…”

Section: Related Workmentioning

confidence: 99%

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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An Accurate Multi-sensor Multi-target Localization Method for Cooperating Vehicles

Cited by 4 publications

References 17 publications

Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Cooperative vehicle localisation method based on the fusion of GPS, inter‐vehicle distance, and bearing angle measurements

Multimodal Multiobject Tracking by Fusing Deep Appearance Features and Motion Information

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

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