Comparison of Vehicle Detection Techniques applied to IP Camera Video Feeds for use in Intelligent Transport Systems

Bugeja, Mark; Dingli, Alexiei; Attard, Maria; Seychell, Dylan

doi:10.1016/j.trpro.2020.02.069

Cited by 13 publications

(6 citation statements)

References 14 publications

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“…The researchers suggested an IoT-enabled control system ( Bugeja et al, 2020 ) to acquire, manage, and aggregate real-world traffic conditions. Their main objective was to improve the range of motion while disseminating important traffic information about traffic jams and unforeseen crashes via the highway signaling system.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based IoT system for secure traffic management and accident detection in smart cities

Balasubramanian

Balaji

Munshi

et al. 2023

PeerJ Computer Science

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In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an Adaptive Traffic Management system (ATM) with an accident alert sound system (AALS) is used for managing traffic congestion and detecting the accident. For secure traffic data transmission Secure Early Traffic-Related EveNt Detection (SEE-TREND) is used. The design makes use of several scenarios to address every potential problem with the transportation system. The suggested ATM model continuously modifies the timing of traffic signals based on the volume of traffic and anticipated movements from neighboring junctions. By progressively allowing cars to pass green lights, it considerably reduces traveling time. It also relieves traffic congestion by creating a seamless transition. The results of the trial show that the suggested ATM system fared noticeably better than the traditional traffic-management method and will be a leader in transportation planning for smart-city-based transportation systems. The suggested ATM-ALTREND solution provides secure traffic data transmission that decreases traffic jams and vehicle wait times, lowers accident rates, and enhances the entire travel experience.

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Section: Related Workmentioning

confidence: 99%

Machine learning based IoT system for secure traffic management and accident detection in smart cities

Balasubramanian

Balaji

Munshi

et al. 2023

PeerJ Computer Science

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“…is the translation vector. Also, can be expressed by equation (18) as the general 2D form, or in the form of equation ( 19) by the use of homogeneous coordinates, were considering the geometric transformations: translation, rotation, scaling, stretching, and shearing; the affine transform has six parameters, u 2 and u 5 for translation, u 0 for aggregate rotation, u 1 for scaling, u 3 for stretching, and u 5 for shearing 54 x y…”

Section: Image Registration Processmentioning

confidence: 99%

“…Most of the methods based on computer vision are video feed and mainly rely on digital image processing, artificial intelligence, and data mining–related technologies. 18 For the development of this article, a VBI assessment based on the processing of vehicle traffic images has been selected. The decision was taken considering that nowadays the tendency of modern cities that contain bridges in their transportation systems is adopting smart city concepts.…”

Section: Introductionmentioning

confidence: 99%

A structural health monitoring method proposal based on optical scanning and computational models

Flores-Fuentes

Alba-Corpus

Sergiyenko

et al. 2022

International Journal of Distributed Sensor Networks

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This article proposes a method for continuous bridge displacement monitoring combining the dynamic triangulation scanning and load estimation by vehicle image recognition. The vehicle–bridge interaction is a non-stationary dynamic process parameter of high relevance to understanding the static instability behavior of bridges. The knowledge of the load on a bridge in the specific time when its structural spatial coordinates are measured allows correlating the bridge displacement with the effect of vehicle–bridge interaction. The evaluation of such correlation is mandatory in order to verify if the observed bridge displacement is due to the nature of its operation or due to it is presenting structural damage. The proposed method is continuous structural health monitoring method, based on the combination of three approaches evaluated at laboratory environment: (1) a three-dimensional optical scanning system for displacement measurement, (2) a load measurement system for vehicle–bridge interaction assessment, and (3) a two-measurement systems data correlation; to be implemented in bridges at a real environment to collect their historical behavior. Overall, for each approach, the measurement systems’ principles, the laboratory experimental methodology followed, and results obtained are presented.

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“…With the rapid development of computer technology, machine learning algorithms have also been widely used in the field of image recognition. Reference [12] compares several commonly used video vehicle recognition methods including deep learning models and computer vision methods. e average accuracy, the semantics of recognizing vehicles, and the robustness of recognition when applied to data sets containing images with different lighting conditions are used to compare detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Image Detection Method Using Deep Learning in UAV Video

Wang

2022

Computational Intelligence and Neuroscience

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Traditional machine learning algorithms are susceptible to objective factors such as video quality and weather environment in the vehicle detection of Unmanned Aerial Vehicle (UAV) videos, resulting in poor detection results. A vehicle image detection method using deep learning in UAV video is proposed. The algorithm in this paper treats surveillance video as many frames of images for vehicle detection in the image. First, perform HSV (Hue-Saturation-Value) spatial brightness translation operation on the original sample to increase the adaptability to different light conditions and sample diversity. Then, the Single Shot MultiBox Detector (SSD) model framework is used as the basis for vehicle detection. In order to obtain a better feature extraction effect, focus loss is added to the basic SSD for optimization. Finally, the trained network model is used to analyze the UAV video, and the detection performance is analyzed experimentally. The results show that the vehicle detection rate of this algorithm has reached 96.49%. It can ensure that the vehicle is accurately detected from the drone video.

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Comparison of Vehicle Detection Techniques applied to IP Camera Video Feeds for use in Intelligent Transport Systems

Cited by 13 publications

References 14 publications

Machine learning based IoT system for secure traffic management and accident detection in smart cities

Machine learning based IoT system for secure traffic management and accident detection in smart cities

A structural health monitoring method proposal based on optical scanning and computational models

Vehicle Image Detection Method Using Deep Learning in UAV Video

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