Cooperative Caching in a Content Centric Network for Video Stream Delivery

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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ALGORITHMS FOR IMAGE PRE-PROCESSING IN THE FACE IDENTIFICATION SYSTEM IN THE VIDEO STREAM

Cherepovets State University Bulletin ◽

10.23859/1994-0637-2019-4-91-2 ◽

2019 ◽

Vol 4 (91) ◽

pp. 21-29 ◽

Cited By ~ 1

Author(s):

Yaroslav Trofimenko ◽

Lyudmila Vinogradova ◽

Evgeniy Ershov

Keyword(s):

Video Stream ◽

Face Identification ◽

Identification System ◽

The Face

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ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

Computer systems and network ◽

10.23939/csn2020.01.080 ◽

2017 ◽

Vol 2 (1) ◽

pp. 80-87

Author(s):

Puyda V. ◽

◽

Stoian. A.

Keyword(s):

Computer Vision ◽

Object Detection ◽

Open Source ◽

Feature Detection ◽

Video Stream ◽

Object Identification ◽

Vision Systems ◽

Modern Computer ◽

Computer Vision Systems ◽

Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

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Human action recognition using simple geometric features and a finite state machine

Image Processing & Communications ◽

10.2478/v10248-012-0079-y ◽

2013 ◽

Vol 18 (2-3) ◽

pp. 49-60 ◽

Cited By ~ 2

Author(s):

Damian Dudzńiski ◽

Tomasz Kryjak ◽

Zbigniew Mikrut

Keyword(s):

Action Recognition ◽

Finite State Machine ◽

Recognition Rate ◽

Human Action Recognition ◽

Human Action ◽

Video Stream ◽

State Machine ◽

Recognition Algorithm ◽

Finite State ◽

Correct Recognition Rate

Abstract In this paper a human action recognition algorithm, which uses background generation with shadow elimination, silhouette description based on simple geometrical features and a finite state machine for recognizing particular actions is described. The performed tests indicate that this approach obtains a 81 % correct recognition rate allowing real-time image processing of a 360 X 288 video stream.

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Improved 2Tier cooperative caching scheme for internet based vehicular adhoc networks (IVANETs)

2017 International Conference on Computing, Communication and Automation (ICCCA) ◽

10.1109/ccaa.2017.8229882 ◽

2017 ◽

Author(s):

Nisha Wadhawan ◽

Meenu Dave

Keyword(s):

Cooperative Caching ◽

Caching Scheme ◽

Adhoc Networks ◽

Vehicular Adhoc Networks

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An Efficient Radio Frequency Interference (RFI) Recognition and Characterization Using End-to-End Transfer Learning

Applied Sciences ◽

10.3390/app10196885 ◽

2020 ◽

Vol 10 (19) ◽

pp. 6885

Author(s):

Sahar Ujan ◽

Neda Navidi ◽

Rene Jr Landry

Keyword(s):

Feature Extraction ◽

Radio Frequency ◽

Transfer Learning ◽

Communication Networks ◽

Continuous Wave ◽

Critical Role ◽

Video Stream ◽

Radio Frequency Interference ◽

Wave Interference ◽

Continuous Wave Interference

Radio Frequency Interference (RFI) detection and characterization play a critical role in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, we propose an efficient Deep Learning (DL)-based methodology using transfer learning to determine both the type of received signals and their modulation type. To this end, the scalogram of the received signals is used as the input of the pretrained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pretrained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

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