Autonomous Multi-sensor Vehicle Classification for Traffic Monitoring

Bischof, Horst; Godec, Martin; Leistner, Christian; Hennecke, Marcus E.; Maier, Arnold; Wolf, J.K.; Rinner, Bernhard; Starzacher, Andreas

doi:10.1007/978-3-642-15503-1_2

Cited by 2 publications

(3 citation statements)

References 16 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%

“…1 (c)) to accumulate on the lens or sensor, affecting image clarity or suffering from clipping, and reducing detail in overexposed situations, increasing the amount of incorrect classifications, as well as the number of false negatives. Previous works have shown that combining multiple sensors can be effective [28]- [31]. A recent work published by researchers at Guilin University of Electronic Technology [32] showed promising results.…”

Section: Hardware Comparison -Efficiencymentioning

confidence: 99%

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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%

Section: Hardware Comparison -Efficiencymentioning

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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“…To achieve this, we design two scenarios (static and dynamic) in this work to install a RF receiver either close to the road or inside the car to aggregate the emitted RF signals from the vehicles. In this work, we evaluate the recognition performance in both static and dynamic scenarios and discuss more about the three classification methods [5]: naive Bayes [6][7][8][9], decision tree [10][11][12] and k-nearest-neighbor [6,[13][14][15], to show the differences (advantage and disadvantage) of various classification algorithms in reality of traffic monitoring. As implemented, these classification methods are applied on the aggregated signal in the computer attached to the RF receiver to classify the traffic situation in both scenarios.…”

Section: Introductionmentioning

confidence: 99%

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

Ding

Banitalebi

Miyaki

et al. 2012

J Wireless Com Network

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In this article, a new traffic sensing and monitoring technique is introduced which works based on the emitted RF noise from the vehicles. In comparison with the current traffic sensing systems, our light-weight technique has simpler structure in both terms of hardware and software. An antenna installed to the roadside or the inside of a car receives the signal generated during electrical activity of the vehicles' sub-systems. This signal feeds the feature extraction and classification blocks which recognize different classes of traffic situation in terms of density, flow and location. Different classifiers like naive Bayes, Decision Tree and k-Nearest Neighbor are applied in real-world scenarios and performances for instance of traffic situation detection are reported with higher than 95%. Although the electrical noises of the various vehicles do not have the same statistical characteristics, results from two experiments with an implementation on RF receiver illustrate that our approach is practically feasible for traffic monitoring goals. Due to the acceptable classification results and the differences between the proposed and current traffic monitoring techniques in terms of interfering factors, advantages and disadvantages, we propose it to work in parallel with the current systems to improve the coverage and efficiency of the traffic control network.

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Autonomous Multi-sensor Vehicle Classification for Traffic Monitoring

Cited by 2 publications

References 16 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

RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

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