Detection of Moving Vehicles on Highway using Fuzzy Logic for Smart Surveillance System

The computer vision incorporates a prominent performance in developing models for medical, security and lot more concepts and we need to deal with detecting and tracking the moving objectsuch as moving vehicle or person. Various challenges are available due to environmental issues, illumination variation, or fast motion etc. This paper work has developed a fuzzy logic based method for identifying the moving vehicles though bounding boxas depicted in the green colour. The paper depictsa fuzzy based method to detect and track the object around the location until the specific secured dimension of the device. The extended recognition (EIR) is the proposed method which works on the automatic fuzzy set creation.The EIR consists of the pixels of the surroundings and recognize them with the predefined inputs we have with the EIR algorithm in the repository. This methodology successfully identified and detects vehicles. It can track the instances within that visible region and this is working as a human eye mechanism.

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Moving Vehicles Detection in Traffic Video Using Modified SXCS-LBP Texture Descriptor

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2015070102 ◽

2015 ◽

Vol 5 (2) ◽

pp. 14-34

Author(s):

Arun Kumar H. D. ◽

Prabhakar C. J.

Keyword(s):

Background Modeling ◽

Experimental Result ◽

Texture Descriptor ◽

Illumination Variation ◽

Moving Vehicle ◽

Moving Vehicles ◽

Spatially Extended ◽

Traffic Video ◽

Lbp Descriptor ◽

Moving Vehicle Detection

Background modeling and subtraction based method for moving vehicle's detection in traffic video using a novel texture descriptor called as Modified Spatially eXtended Center Symmetric Local Binary Pattern (Modified SXCS-LBP) descriptor. The XCS-LBP texture descriptor is sensitive to noise because in order to generate binary code, the value of center pixel value is used as the threshold directly, and it does not consider temporal motion information. In order to solve this problem, this paper proposed a novel texture descriptor called as Modified SXCS-LBP descriptor for moving vehicle detection based on background modeling and subtraction. The proposed descriptor is robust against noise, illumination variation, and able to detect slow moving vehicles because it considers both spatial and temporal moving information. The evaluation carried out using precision and recall metric, which are obtained using experiments conducted on two popular datasets such as BMC and CDnet datasets. The experimental result shows that the authors' method outperforms existing texture and non-texture based methods.

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Remote Fault-Tolerant Control for Industrial Smart Surveillance System

Mathematical Problems in Engineering ◽

10.1155/2021/9927113 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Atif Mahmood ◽

Abdul Qayyum Khan ◽

Ghulam Mustafa ◽

Nasim Ullah ◽

Muhammad Abid ◽

...

Keyword(s):

Surveillance System ◽

Fault Tolerant ◽

Sufficient Conditions ◽

Fault Tolerant Control ◽

Linear Matrix ◽

Control Law ◽

Uncertain Network ◽

Smart Surveillance ◽

Communication Links ◽

Smart Surveillance System

We design a remote fault-tolerant control for an industrial surveillance system. The designed controller simultaneously tolerates the effects of local faults of a node, the propagated undesired effects of neighboring connected nodes, and the effects of network-induced uncertainties from a remote location. The uncertain network-induced time delays of communication links from the sensor to the controller and from the controller to the actuator are modeled using two separate Markov chains and packet dropouts using the Bernoulli process. Based on linear matrix inequalities, we derive sufficient conditions for output feedback-based control law, such that the controller does not directly depend on output, for stochastic stability of the system. The simulation study shows the effectiveness of the proposed approach.

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IoT Based Smart Surveillance System for Healthcare Monitoring using Raspberry PI

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9974.1081219 ◽

2019 ◽

Vol 8 (12) ◽

pp. 627-629

Keyword(s):

Blood Pressure ◽

Surveillance System ◽

Heart Diseases ◽

Electrical Signal ◽

Raspberry Pi ◽

Electronic Board ◽

Pulse Sensor ◽

Smart Surveillance ◽

Healthcare Monitoring ◽

Smart Surveillance System

IOT could be a trending in technology that can transform any device into a wise one a lot of industries setting out to utilize these technologies to extend their capacity and improve potency. These system has been created to detect people who are suffering with heart diseases, this framework is powered by Raspberry pi electronic board, which is worked on power control supply, Remote web availability by utilizing USB modem, it incorporates with sensors. pulse sensor which detects each beats per minute price. Temperature sensor detects the temperature variation, blood pressure sensor reads blood pressure and heart rate, ECG sensor which measures the electrical signal of the heart. it is an analog from converted in digital by using of SPI protocol. If any emergency occurs, it will raise a caution send it to the website and mobile though NOOBS Software. If any sensor parameter value more than the instructed value it will raise a beep sound

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Moving Vehicle Detection Using Time Domain Statistical Features

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.471.208 ◽

2013 ◽

Vol 471 ◽

pp. 208-212 ◽

Cited By ~ 1

Author(s):

M.P. Paulraj ◽

Hamid Adom Abdul ◽

Marhainis Othman Siti ◽

Sundararaj Sathishkumar

Keyword(s):

Probabilistic Neural Network ◽

Hearing Impaired ◽

Statistical Features ◽

Experimental Protocol ◽

Moving Vehicle ◽

Vehicle Type ◽

Moving Vehicles ◽

Machine Interface ◽

Moving Vehicle Detection ◽

Sound Signature

The Hearing Impaired People (HIP) cannot distinguish the sound from a moving vehicle approaching from their behind. Since, it is difficult for hearing impaired to hear and judge sound information and they often encounter risky situations while they are in outdoor. If HIPs can successfully get sound information through some machine interface, dangerous situation will be avoided. Generally the profoundly deaf people do not use any hearing aid which does not provide any benefit. This paper presents, simple statistical features are used to classify the vehicle type and its distance based on sound signature recorded from the moving vehicles. An experimental protocol is designed to record the vehicle sound under different environment conditions and also at different speed of vehicles. Basic statistical features such as the standard deviation, Skewness, Kurtosis and frame energy have been used to extract the features. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation.

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