Single Run Action Detector over Video Stream - A Privacy Preserving Approach

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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Privacy Preserving Public Auditing in Secured Cloud Storage

International Journal of Scientific Research ◽

10.15373/22778179/march2014/18 ◽

2012 ◽

Vol 3 (3) ◽

pp. 60-61

Author(s):

V.Sajeev V.Sajeev ◽

◽

R.Gowthamani R.Gowthamani

Keyword(s):

Cloud Storage ◽

Privacy Preserving ◽

Public Auditing

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Adaptive Random Decision Tree: A New Approach for Data Mining with Privacy Preserving

International Journal of Innovative Research in Computer and Communication Engineering ◽

10.15680/ijircce.2015.0307004 ◽

2015 ◽

Vol 03 (07) ◽

pp. 6378-6384

Author(s):

Hemlata B. Deorukhakar, Prof. Pradnya Kasture

Keyword(s):

Data Mining ◽

Decision Tree ◽

Privacy Preserving ◽

New Approach

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Privacy-Preserving Fingerprint Authentication Resistant to Hill-Climbing Attacks

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e101.a.138 ◽

2018 ◽

Vol E101.A (1) ◽

pp. 138-148 ◽

Cited By ~ 1

Author(s):

Haruna HIGO ◽

Toshiyuki ISSHIKI ◽

Kengo MORI ◽

Satoshi OBANA

Keyword(s):

Privacy Preserving ◽

Hill Climbing

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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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