An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

Appathurai, Ahilan; Sundarasekar, Revathi; Raja, Chandrasekaran; Alex, E. John; Palagan, C. Anna; Nithya, A.

doi:10.1007/s00034-019-01224-9

Cited by 60 publications

(20 citation statements)

References 30 publications

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“…The results show that the THD capability of the optimized filter is far better than of the improvised LCL filter. 7 Hardware Results dSPACE hardware-in-the-loop based prototyping is used to realize the 5-H Bridge Multi-Level Inverter [20][21][22] system with optimized filter which allows us to have better comparison between simulation and hardware results. The modules in the setup include a dSPACE 1104 type real-time hardware, relay circuits, PV Emulator, Semikron inverter, voltage and current sensor for feedback purpose, LCL filter and a PC which consists of control software which has the capability to run MATLAB simulation of the given system.…”

Section: Simulation Resultsmentioning

confidence: 99%

Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller

Vijayal¹,

Krishnamoorthi²

2022

Intelligent Automation &Amp; Soft Computing

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In high power medium voltage applications, the utilization of 5-H Bridge Multi-Level Inverter (MLI) has grown vastly in recent years. The 5-H Bridge MLI can effectively control link voltage as well as power factor. However, the inverter imparts harmonics owing to the high switching frequency. Hence Inductance Capacitance Inductance (LCL) filter is implemented at its output to mitigate harmonics in presence of non-linear load. It's of highly important to choose LCL parameters wisely in order to attain good filtering effect. This work investigates the application of 5-H Bridge MLI with LCL filter at the output for efficient integration of renewable energy sources on to the grid. A modified fuzzy heuristic approach is used to solve LCL power filter optimization problem at output of 5-H Bridge Multi-Level Inverter. Subject to the constraint's harmonic distortion and losses of the inverter, multi objective optimization problem is solved to obtain LCL filter optimal values. The optimized LCL filter design was applied to a grid connected cascaded H-bridge MLI of rating 4.5 MW, 11 kV. MATLAB-Simulink is utilized for modelling and simulating the system. The results showed that the system is more efficient and generic. dSPACE Hardware in Loop (HIL) test system was used to verify the efficacy of the prototype system.

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Section: Simulation Resultsmentioning

confidence: 99%

Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller

Vijayal¹,

Krishnamoorthi²

2022

Intelligent Automation &Amp; Soft Computing

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“…State-of-the-art deep learning techniques are mainly used for image recognition and classification. We use a smart camera app "calorie mama" that is excelled in deep learning to track nutrition from food images [16,17] "Calorie Mama App" is powered by our Food AI API [18]. Food AI API is found on the latest innovations in deep learning and image classification technology to identify food items quickly and accurately.…”

Section: Calorie Measuring Systemmentioning

confidence: 99%

To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

Viveka¹,

Columbus²,

Velmurugan³

2022

Intelligent Automation &Amp; Soft Computing

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Because of the increasing workload, people are having several clinical examinations to determine their health status, resulting in limited time. Here, we present a healthful consuming device based on rule mining that can modify your parameter dependency and recommend the varieties of meals that will boost your fitness and assist you to avoid the types of meals that increase your risk for sicknesses. Using the meals database, the data mining technique is useful for gathering meal energy from breakfast, after breakfast, lunch, after lunch, dinner, after dinner, and bedtime for ninety days. The purpose of this study is to determine to mean random plasma glucose levels and h1bc levels using the Nathan, ADAG (A1C-derived average glucose), and DTTC (Dynamic Temporal and Tactile Cueing) methods. This system can identify and recognize food images, as well as keep track of the food items ingested by the user. Deep learning techniques are mostly utilized for picture recognition and categorization. The KNN (k-nearest neighbors algorithm) classification approach is used to determine if diabetes is normal, pre-diabetic, or chronic. This study employs deep learning and a smart camera app called "calorie mom" to track nutrition from meal photographs. In addition, the commonly used measures of divisions such as accuracy, sensitivity, uniqueness, and recalling diabetic dataset using Python 3 Jupyter Notebook were employed to evaluate the performance of a machine learning classifier.

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“…Their model was three times faster and also smaller than YoloV3. The authors in [5] designed a moving vehicle detection system based on an artificial neural network (ANN) and oppositional gravitational search optimization algorithm (ANN-OGSA). The system first generates the background for the vehicle, followed by the vehicle detection by the algorithm.…”

Section: Related Workmentioning

confidence: 99%

Comparative Study of Vehicle Detection using SSD and Faster RCNN

Krishna¹,

Sowmya²,

Jerusha³

et al. 2021

IJCSMC

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With the recent developments in Artificial Intelligence, vehicle detection systems have become an essential part of many sectors like transport, automobile, security, law enforcement, and traffic management. This increased the requirement for an efficient system for vehicle detection. The main focus of our work is to find the best algorithm which can be used to design a vehicle detection system. For this, we compare two well-known deep learning algorithms which are Faster R-CNN and Single Shot Detector (SSD) algorithms. Both of the Pre-trained models of Tensorflow were tested on a dataset of hundred images with cars in them. It was found that Faster R-CNN is better with an accuracy score of 82.75 but was slower than SSD, whereas SSD had an accuracy score of 80.58 but was faster compared to Faster R-CNN.

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An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

Cited by 60 publications

References 30 publications

Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller

Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller

To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

Comparative Study of Vehicle Detection using SSD and Faster RCNN

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An Efficient Optimal Neural Network-Based Moving Vehicle Detection in Traffic Video Surveillance System

Cited by 60 publications

References 30 publications

Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller

Integrated Renewable Smart Grid System Using Fuzzy Based Intelligent Controller

To Control Diabetes Using Machine Learning Algorithm and Calorie Measurement Technique

Comparative Study of Vehicle Detection using SSD and Faster RCNN﻿

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Product

Resources

About

Comparative Study of Vehicle Detection using SSD and Faster RCNN