Online Multipedestrian Tracking Based on Fused Detections of Millimeter Wave Radar and Vision

Cui, Fucheng; Zhang, Qiang; Wu, Jingxuan; Song, Yuying; Xie, Zhouzhen; Song, Chunyi; Xu, Zhiwei

doi:10.1109/jsen.2023.3255924

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…Table 1provides a comparative overview of the influence of weather conditions by sensor type. Recently, multi-sensor-based traffic monitoring systems have begun to gain more traction [28], [29], [33]- [35]. The primary motivation behind this trend is to improve detection accuracy and mitigate the shortcomings in complex, changing environments and weather conditions [30]- [32].…”

Section: Related Workmentioning

confidence: 99%

Open Urban mmWave Radar and Camera Vehicle Classification Dataset for Traffic Monitoring

Soom,

Leier,

Janson

et al. 2024

IEEE Access

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Traffic monitoring systems featuring robust, multi-sensor fusion capabilities are rapidly growing in demand to observe traffic flow, reduce congestion and to detect and report traffic accidents. However, monitoring outdoor environments using cameras remains challenging due to complex weather conditions, including fog, rain, snow and variable lighting conditions. The presence of these weather conditions can significantly reduce vehicle detection and classification performance using machine learning methods. Unfortunately, openly available datasets for multi-sensor traffic monitoring development and testing remain limited, especially those featuring infrastructure-based cameras and millimeter wave (mmWave) radar. To address these challenges, we evaluate open camera and mmWave radar data using vehicle classification models for cars, trucks, vans and buses on embedded hardware. We also provide an open multi-sensor traffic monitoring dataset with more than 8,000 manually annotated frames as well as mmWave radar point clouds recorded in an urban environment under sunny, partially cloudy, cloudy, rainy and night conditions.

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Section: Related Workmentioning

confidence: 99%

Open Urban mmWave Radar and Camera Vehicle Classification Dataset for Traffic Monitoring

Soom,

Leier,

Janson

et al. 2024

IEEE Access

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“…In recent years, the rapid advancement of millimeter-wave technology has brought cost-effective, compact, and powerful millimeter-wave sensors into the mainstream, with millimeter-wave radar being the most widely used among them. Millimeter-wave radar is not only capable of measuring target distance, velocity, angle, positioning [1] and tracking [2] information but also finds extensive applications in human detection [3], behavior recognition [4], [5], micro-motion detection [6], and vital sign monitoring [7], [8].Among, one of the primary reasons millimeter-wave radar can achieve…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Filtering Algorithm Based on Range-Doppler Information Guidance

Li,

Zhang,

et al. 2023

IEEE Access

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Millimetre-wave frequency-modulated continuous-wave (FMCW) radar is widely used in various scenarios. However, it is often affected by static and dynamic clutter interference, which has a negative impact on its performance. Specifically, these clutter signals are often mistaken for target signals, leading to false detection and affecting the accuracy of target tracking and localization. In addition, dynamic clutter sources, such as other moving objects, also bring about Doppler frequency shift interference, further affecting the measurement of target velocity. In this paper, addressing the issue of static clutter, we propose a frame mean subtraction method. Additionally, for the more complex problem of dynamic clutter, we introduce a filtering approach guided by distance-Doppler information. This method utilizes a mask generated in real-time by tracking the temporal distance information of the target as prior information for filtering radar signals. Subsequently, we employ a novel fractional short-time Fourier transform to extract the Doppler feature spectrogram of the radar signal. Finally, a ResNet-50 model trained on the Doppler spectrograms of interference-free radar signals is used to test the Doppler maps generated from the filtered radar signals. After testing, the classification accuracy reaches 97.5%. This result shows that the micro-Doppler spectrum obtained by filtering the radar signal collected in complex scenes using the proposed method is highly similar to the micro-Doppler spectrum of the target to be measured. In addition, the proposed filtering method not only plays the role of signal filtering, but also enhances the strength of the target signal and provides more detailed information for the subsequent recognition task.INDEX TERMS LFMCW, micro-doppler, radar signal filtering, convolutional neural network.

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Target Track Enhancement Based on Asynchronous Radar and Camera Fusion in Intelligent Driving System

Wang,

Chu,

Guo

et al. 2024

IEEE Sensors J.

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Online Multipedestrian Tracking Based on Fused Detections of Millimeter Wave Radar and Vision

Cited by 3 publications

References 34 publications

Open Urban mmWave Radar and Camera Vehicle Classification Dataset for Traffic Monitoring

Open Urban mmWave Radar and Camera Vehicle Classification Dataset for Traffic Monitoring

An Adaptive Filtering Algorithm Based on Range-Doppler Information Guidance

Target Track Enhancement Based on Asynchronous Radar and Camera Fusion in Intelligent Driving System

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