Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors

Odat, Enas M.; Shamma, Jeff S.; Claudel, Christian

doi:10.1109/tits.2017.2727224

Cited by 117 publications

(58 citation statements)

References 29 publications

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“…At present, the sensing sensors used in autonomous vehicles mainly include Lidar, millimeter-wave radar, ultrasonic radar, and camera [2][3][4][5]. Lidar can scan and measure by transmitting laser pulses to generate a precise map of road scene topography, which can be used for short-distance and long-distance obstacle detection.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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Vehicle detection is a crucial task for autonomous driving and demands high accuracy and real-time speed. Considering that the current deep learning object detection model size is too large to be deployed on the vehicle, this paper introduces the lightweight network to modify the feature extraction layer of YOLOv3 and improve the remaining convolution structure, and the improved Lightweight YOLO network reduces the number of network parameters to a quarter. Then, the license plate is detected to calculate the actual vehicle width and the distance between the vehicles is estimated by the width. This paper proposes a detection and ranging fusion method based on two different focal length cameras to solve the problem of difficult detection and low accuracy caused by a small license plate when the distance is far away. The experimental results show that the average precision and recall of the Lightweight YOLO trained on the self-built dataset is 4.43% and 3.54% lower than YOLOv3, respectively, but the computing speed of the network decreases 49 ms per frame. The road experiments in different scenes also show that the long and short focal length camera fusion ranging method dramatically improves the accuracy and stability of ranging. The mean error of ranging results is less than 4%, and the range of stable ranging can reach 100 m. The proposed method can realize real-time vehicle detection and ranging on the on-board embedded platform Jetson Xavier, which satisfies the requirements of automatic driving environment perception.

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Section: Introductionmentioning

confidence: 99%

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Liu

Zhang

2020

Journal of Sensors

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“…Some of the most widely used sensors include magnetometers [21] [22], accelerometers [23], and acoustic sensors [24]. Recently, advanced sensors such as Laser Infrared Detection and Ranging (LIDAR) [25] [4], infrared sensors [26], and Wi-Fi transceivers [27] have been employed. Despite the benefits of easier installation and reduced cost, the side-roadwaybased systems require extra efforts for adjusting precisely the directions and placement of the sensors [26].…”

Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

“…Recently, advanced sensors such as Laser Infrared Detection and Ranging (LIDAR) [25] [4], infrared sensors [26], and Wi-Fi transceivers [27] have been employed. Despite the benefits of easier installation and reduced cost, the side-roadwaybased systems require extra efforts for adjusting precisely the directions and placement of the sensors [26]. A more critical problem is that most systems fail to classify overlapped vehicles accurately.…”

Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

151

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A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality-vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of the vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.

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“…The existing vehicle speed estimation system includes buried induction loop technology [4], radar technology [5], ultrasonic technology [6,7], laser technology [8], video-based technology [2], and so on [9][10][11]. Among these, geomagnetic coil technology is widely used, but these technologies require extensive installation and maintenance of equipment and the use of professional knowledge for configuration and calibration.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Zhang

et al. 2019

Remote Sensing

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With the rapid development of unmanned aerial vehicles (UAVs), UAV-based intelligent airborne surveillance systems represented by real-time ground vehicle speed estimation have attracted wide attention from researchers. However, there are still many challenges in extracting speed information from UAV videos, including the dynamic moving background, small target size, complicated environment, and diverse scenes. In this paper, we propose a novel adaptive framework for multi-vehicle ground speed estimation in airborne videos. Firstly, we build a traffic dataset based on UAV. Then, we use the deep learning detection algorithm to detect the vehicle in the UAV field of view and obtain the trajectory in the image through the tracking-by-detection algorithm. Thereafter, we present a motion compensation method based on homography. This method obtains matching feature points by an optical flow method and eliminates the influence of the detected target to accurately calculate the homography matrix to determine the real motion trajectory in the current frame. Finally, vehicle speed is estimated based on the mapping relationship between the pixel distance and the actual distance. The method regards the actual size of the car as prior information and adaptively recovers the pixel scale by estimating the vehicle size in the image; it then calculates the vehicle speed. In order to evaluate the performance of the proposed system, we carry out a large number of experiments on the AirSim Simulation platform as well as real UAV aerial surveillance experiments. Through quantitative and qualitative analysis of the simulation results and real experiments, we verify that the proposed system has a unique ability to detect, track, and estimate the speed of ground vehicles simultaneously even with a single downward-looking camera. Additionally, the system can obtain effective and accurate speed estimation results, even in various complex scenes.

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Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors

Cited by 117 publications

References 29 publications

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Vehicle Detection and Ranging Using Two Different Focal Length Cameras

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

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

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