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 2 publications

(1 citation statement)

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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%