Robust Real‐Time Traffic Surveillance with Deep Learning

Fernandez, J.D.; Plaza, José María; Fernández, Vanessa; Paniego, Sergio

doi:10.1155/2021/4632353

Cited by 11 publications

(13 citation statements)

References 58 publications

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“…Object tracking is usually preceded by object detection. Fernández et al (2021) present TrafficSensor, a system that employs DL techniques for automatic vehicle tracking and classification on highways using a calibrated and fixed camera. TrafficSensor accurately detects and classifies the objects within images using various versions of YOLO.…”

Section: Object Tracking In Videomentioning

confidence: 99%

Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

Quinn

Corcoran

2022

ECIAIR

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This study examines the potential applications of Human Action Recognition (HAR) in combat sports and aims to develop a prototype automation client that examines a video of a combat sports competition or training session and accurately classifies human movements. Computer Vision (CV) architectures that examine real-time video data streams are being investigated by integrating Deep Learning architectures into client-server systems for data storage and analysis using customised algorithms. The development of the automation client for training and deploying CV robots to watch and track specific chains of human actions is a central component of the project. Categorising specific chains of human actions allows for the comparison of multiple athletes' techniques as well as the identification of potential areas for improvement based on posture, accuracy, and other technical details, which can be used as an aid to improve athlete efficiency. The automation client will also be developed for the purpose of scoring, with a focus on the automation of the CV model to analyse and score a competition using a specific ruleset. The model will be validated by comparing performance and accuracy to that of combat sports experts. The primary research domains are CV, automation, robotics, combat sports, and decision science. Decision science is a set of quantitative techniques used to assist people to make decisions. The creation of a new automation client may contribute to the development of more efficient machine learning and CV applications in areas such as process efficiency, which improves user experience, workload management to reduce wait times, and run-time optimisation. This study found that real-time object detection and tracking can be combined with real-time pose estimation to generate performance statistics from a combat sports athlete's movements in a video.

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Section: Object Tracking In Videomentioning

confidence: 99%

Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

Quinn

Corcoran

2022

ECIAIR

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“…This section presents the use of Detection Metrics in the development of a real application, Smart-Traffic-Sensor [47]), proving its usefulness and the added value it provides. This real application monitors road traffic using computer vision.…”

Section: Usage On a Deep Learning Real Research Applicationmentioning

confidence: 99%

“…The models were generated using different deep learning frameworks, showing that Detection Metrics supports all of them. A new dataset with 9774 real traffic images was also created using Detection Metrics [47]. In Table 2, the comparison of the used networks, obtained using the automatic Headless evaluation, is shown.…”

Section: Usage On a Deep Learning Real Research Applicationmentioning

confidence: 99%

Open Source Assessment of Deep Learning Visual Object Detection

Paniego

Sharma

Plaza

2022

Sensors

Self Cite

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This paper introduces Detection Metrics, an open-source scientific software for the assessment of deep learning neural network models for visual object detection. This software provides objective performance metrics such as mean average precision and mean inference time. The most relevant international object detection datasets are supported along with the most widely used deep learning frameworks. Different network models, even those built from different frameworks, can be fairly compared in this way. This is very useful when developing deep learning applications or research. A set of tools is provided to manage and work with different datasets and models, including visualization and conversion into several common formats. Detection Metrics may also be used in automatic batch processing for large experimental tests, saving researchers time, and new domain-specific datasets can be easily created from videos or webcams. It is open-source, can be audited, extended, and adapted to particular requirements. It has been experimentally validated. The performance of the most relevant state-of-the-art neural models for object detection has been experimentally compared. In addition, it has been used in several research projects, guiding in selecting the most suitable network model architectures and training procedures. The performance of the different models and training alternatives can be easily measured, even on large datasets.

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“…Among several deep learning methods, the YOLO (you only look once) object detection model family [52][53][54][55][56] introduced a new architectural approach that leads to a significant improvement-especially in computation speed-and an easier implementation of realtime analysis systems [57][58][59].…”

Section: Introductionmentioning

confidence: 99%

Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization

Fredianelli

Carpita²,

Bernardini

et al. 2022

Sensors

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Noise maps and action plans represent the main tools in the fight against citizens’ exposure to noise, especially that produced by road traffic. The present and the future in smart traffic control is represented by Intelligent Transportation Systems (ITS), which however have not yet been sufficiently studied as possible noise-mitigation tools. However, ITS dedicated to traffic control rely on models and input data that are like those required for road traffic noise mapping. The present work developed an instrumentation based on low-cost cameras and a vehicle recognition and counting methodology using modern machine learning techniques, compliant with the requirements of the CNOSSOS-EU noise assessment model. The instrumentation and methodology could be integrated with existing ITS for traffic control in order to design an integrated method, which could also provide updated data over time for noise maps and action plans. The test was carried out as a follow up of the L.I.S.T. Port project, where an ITS was installed for road traffic management in the Italian port city of Piombino. The acoustic efficacy of the installation is evaluated by looking at the difference in the acoustic impact on the population before and after the ITS installation by means of the distribution of noise exposure, the evaluation of Gden and Gnight, and the calculation of the number of highly annoyed and sleep-disturbed citizens. Finally, it is shown how the ITS system represents a valid solution to be integrated with targeted and more specific sound mitigation, such as the laying of low-emission asphalts.

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Robust Real‐Time Traffic Surveillance with Deep Learning

Cited by 11 publications

References 58 publications

Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

Automation of Computer Vision Applications for Real-time Combat Sports Video Analysis

Open Source Assessment of Deep Learning Visual Object Detection

Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization

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