Robust Vehicle Tracking with Monocular Vision using Convolutional Neuronal Networks

Dichgans, Jakob; Kallwies, Jan; Wuensche, Hans-Joachim

doi:10.1109/mfi49285.2020.9235213

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…A pedestrian detection algorithm for complex scenes is proposed in Reference [ 9 ], and an algorithm for the appearance and distance of the measurement to be inspected is proposed in Reference [ 10 ]. On the basis of target detection, Reference [ 11 ] then introduces a target-tracking algorithm based on convolutional neural networks. After experiments, it is proved that it can complete the track management and trajectory tracking of quantitative targets based on monocular data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion

Zou

Zheng

Wang

2023

Sensors

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To improve the identification accuracy of target detection for intelligent vehicles, a real-time target detection system based on the multi-source fusion method is proposed. Based on the ROS melodic software development environment and the NVIDIA Xavier hardware development platform, this system integrates sensing devices such as millimeter-wave radar and camera, and it can realize functions such as real-time target detection and tracking. At first, the image data can be processed by the You Only Look Once v5 network, which can increase the speed and accuracy of identification; secondly, the millimeter-wave radar data are processed to provide a more accurate distance and velocity of the targets. Meanwhile, in order to improve the accuracy of the system, the sensor fusion method is used. The radar point cloud is projected onto the image, then through space-time synchronization, region of interest (ROI) identification, and data association, the target-tracking information is presented. At last, field tests of the system are conducted, the results of which indicate that the system has a more accurate recognition effect and scene adaptation ability in complex scenes.

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Section: Introductionmentioning

confidence: 99%

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion

Zou

Zheng

Wang

2023

Sensors

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Prediction Using a Fuzzy Inference System in the Classification Layer of a Convolutional Neural Network Replacing the Softmax Function

Poma,

Melin

2024

Studies in Computational Intelligence

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RPV Determination for Heavy Truck Platooning Applications Using Ir and RGB Monocular Camera

Flegel,

Chen,

Bevly

2024

SAE Technical Paper Series

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<title>ABSTRACT</title> <p>Many significant advances have been made in autonomous vehicle technology over the recent decades. This includes platooning of heavy trucks. As such, many institutions have created their own version of the basic platooning platform. This includes the California PATH program [<xref rid="R1" ref-type="bibr">1</xref>], Japan’s “Energy ITS” project [<xref rid="R2" ref-type="bibr">2</xref>], and Auburn University’sCACC Platform [<xref rid="R3" ref-type="bibr">3</xref>]. One thing these platforms have in common is a strong dependence on GPS based localization solutions. Issues arise when the platoon navigates into challenging environments, including rural areas with foliage which might block receptions, or more populated areas which might present urban canyon effects. Recent research focus has shifted to handling these situations through the use of alternative sensors, including cameras. The perception method proposed in this paper utilizes the You Only Look Once (YOLO) real-time object detection algorithm in order to bound the lead vehicle using both RGB and IR cameras. Range and bearing are determined using various methods. The methods are then tested on real world data.</p> <p><bold>Citation:</bold> T. Flegel, H. Chen, D. Bevly, “RPV Determination for Heavy Truck Platooning Applications Using IR and RGB Monocular Camera,” In <italic>Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium</italic> (GVSETS), NDIA, Novi, MI, Aug. 16-18, 2022.</p>

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Robust Vehicle Tracking with Monocular Vision using Convolutional Neuronal Networks

Cited by 3 publications

References 12 publications

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion

Real-Time Target Detection System for Intelligent Vehicles Based on Multi-Source Data Fusion

Prediction Using a Fuzzy Inference System in the Classification Layer of a Convolutional Neural Network Replacing the Softmax Function

RPV Determination for Heavy Truck Platooning Applications Using Ir and RGB Monocular Camera

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