A Novel Traffic Surveillance System Using an Uncalibrated Camera

Gupta, Sandeep; Kartha, Ranju S; Ramírez-Asís, Edwin; Flores-Albornoz, Judith; Vílchez-Vásquez, Rosa; Singh, Sitesh Kumar

doi:10.1155/2022/5867524

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…A deep learning network based on video data is proposed to achieve road congestion classification and regulation [17]. Traffic congestion mitigation is implemented using road uncalibrated camera systems for vehicle speed and flow estimation based on digital image processing techniques [18]. And multi‐sensor fusion techniques such as video and LIDAR fusion are proposed to achieve road congestion discrimination [19].…”

Section: Introductionmentioning

confidence: 99%

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Yang

Zhang

et al. 2023

IET Radar Sonar & Navi

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The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.

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Section: Introductionmentioning

confidence: 99%

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Yang

Zhang

et al. 2023

IET Radar Sonar & Navi

View full text Add to dashboard Cite

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“…So far, terrorism is a disaster that has affected many countries and taken the lives of countless innocent people [1]. Open areas are characterized by a large number of people, complex composition, and dense distribution; plus, the existence of uncertainty factors can easily become the main target of terrorist attacks [2]. How to prevent terrorist attacks is a global scholarly research focus [3].…”

Section: Introductionmentioning

confidence: 99%

AI-Based W-Band Suspicious Object Detection System for Moving Persons: Two-Stage Walkthrough Configuration and Recognition Optimization

Wen

et al. 2022

Wireless Communications and Mobile Computing

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In recent years, terrorist attacks have been spreading worldwide and become a public hazard to human society. The suspicious object detection system is an effective way to prevent terrorist attacks in public places. However, traditional systems face two main challenges: First, they need to conduct security checks at the entrance one by one, which leads to crowding; second, they rely heavily on screeners’ ability to understand security images, which can easily lead to misjudgment. To address these issues, we propose an AI-based W-band suspicious object detection system for moving persons that can perform a two-stage walkthrough screening for suspicious objects in an open area to maintain high throughput. The 1st screening uses millimeter wave radar and cameras to automatically screen suspects who may have concealed suspicious objects in an open area. The 2nd screening involves security personnel using a hybrid imager with active and passive imaging capabilities to identify the specific suspicious objects carried by the suspect. Convolutional neural network (CNN) based artificial intelligence (AI) technology will be used to improve the accuracy and speed of suspicious object detection. We performed an experiment to validate the proposed system. The usability and safety of the system are demonstrated by recognition rate (aka accuracy rate) or both recall and precision rate. In addition, in the process of improving the suspicious object recognition rate by AI techniques, we use generative adversarial network to help build a suspicious object database and successfully validate the effectiveness of the method and the factors affecting the suspicious object recognition rate to optimize the system.

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Retracted: A Novel Traffic Surveillance System Using an Uncalibrated Camera

2023

Journal of Advanced Transportation

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A Novel Traffic Surveillance System Using an Uncalibrated Camera

Cited by 3 publications

References 17 publications

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

AI-Based W-Band Suspicious Object Detection System for Moving Persons: Two-Stage Walkthrough Configuration and Recognition Optimization

Retracted: A Novel Traffic Surveillance System Using an Uncalibrated Camera

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