A Light Weight Background Subtraction Algorithm for Motion Detection in Fog Computing

2020 ◽  
Vol 3 (1) ◽  
pp. 17-20
Author(s):  
R. Siddharth ◽  
G. Aghila
Keyword(s):  
Motion Detection ◽  
Background Subtraction ◽  
Fog Computing ◽  
Light Weight

Motion Detection of Webcam Using Frame Differencing Method

2021 ◽  
pp. 32-38
Author(s):  
Pooja Nagpal ◽  
Shalini Bhaskar Bajaj ◽  
Aman Jatain ◽  
Sarika Chaudhary
Keyword(s):  
Machine Learning ◽  
Motion Detection ◽  
Background Subtraction ◽  
Surveillance System ◽  
Detection Algorithm ◽  
Alert Message ◽  
Object Level

It is the capability of humans and as well as vehicles to automatically detect object level motion that results into collision less navigation and also provides sense of situation. This paper presents a technique for secure object level motion detection which yields more accurate results. To achieve this, python code has been used along with various machine learning libraries. The detection algorithm uses the advantage of background subtraction and fed in data to detect even the slightest movement this system makes use of a webcam to scan a premise and detect movement of any sort; on the recognition of any activity it immediately sends an alert message to the owner of the system via mail. Any person requiring a surveillance system can use it.

Abnormal Motion Detection for Intelligent Video Surveillance

2011 ◽  
Vol 58-60 ◽  
pp. 2290-2295 ◽  
Author(s):  
Ruo Hong Huan ◽  
Xiao Mei Tang ◽  
Zhe Hu Wang ◽  
Qing Zhang Chen
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Video Surveillance ◽  
Motion Detection ◽  
Background Subtraction ◽  
Moving Objects ◽  
Video Streams ◽  
Intelligent Video Surveillance ◽  
Tracking Process ◽  
Object Parameters

A method of abnormal motion detection for intelligent video surveillance is presented, which includes object intrusion detection, object overlong stay detection and object overpopulation detection. Background subtraction algorithm is used to detect moving objects in video streams. Kalman filter is applied for object tracking. By the construction of relation matrix, the tracking process is divided into five statuses for prediction and estimation, which are object disappearing, object separating, new object appearing, object sheltering and object matching. The object parameters and predictive information in the next frame which is used to track moving objects is established by Kalman filter. Then, three types of abnormal motion detection are implemented. The relative position of alarm area or guard line with the rectangle boxes of the moving objects is used to detect whether the object is invading. The existing time of the moving objects in monitor area is counted to detect whether the object is staying too long. Moving objects in the monitor area are classified and counted to detect whether the objects are too much. Alarm will be triggered when abnormal motion detection as defined is detected in the monitor area.

scholarly journals Review of Human Motion Detection based on Background Subtraction Techniques

2015 ◽  
Vol 122 (13) ◽  
pp. 1-5 ◽  
Author(s):  
Arwa DarwishAlzughaibi ◽  
Hanadi Ahmed Hakami ◽  
Zenon Chaczko
Keyword(s):  
Motion Detection ◽  
Background Subtraction ◽  
Human Motion ◽  
Human Motion Detection

Space Monitoring System Using the Background Subtraction Method with Whatsapp Notifications

2017 ◽  
Vol 5 (2) ◽  
pp. 15-20
Author(s):  
Reza Aulia
Keyword(s):  
Quality Of Service ◽  
Light Intensity ◽  
Motion Detection ◽  
Background Subtraction ◽  
Mobile Network ◽  
Fire Detection ◽  
Subtraction Method ◽  
Service Testing ◽  
Detection Program

This research was carried out by monitoring space using the background subtraction method with WhatsApp notifications, with features that make a system that can work and can help security and safety. The testing of this research is the effect of light intensity, the diversity of objects with different distances on motion detection and fire detection, WhatsApp notification delay testing and Quality of Service streaming networks on the website. From the results of the system testing carried out, the test results show that the light intensity used in the motion detection program must be more than 0 lux and objects that are too small are not defined as motion, fire detection can work at lux 8.33 and 25, delay in sending notifications is the same - equally good, when using a mobile network or FTTH, for delay in QOS (Quality of Service) testing it is in the very bad category, namely 0.99 Second, the resulting throughtput is 1048.53 Bytes / second on average and Packet loss is categorized as good in ITU -T with a value of 0%.

scholarly journals A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm

Sensors ◽  
2010 ◽  
Vol 10 (2) ◽  
pp. 1041-1061 ◽  
Author(s):  
Parisa Darvish Zadeh Varcheie ◽  
Michael Sills-Lavoie ◽  
Guillaume-Alexandre Bilodeau
Keyword(s):  
Motion Detection ◽  
Background Subtraction

Motion detection based on the combining of the background subtraction and the structure–texture decomposition

Optik ◽  
2015 ◽  
Vol 126 (24) ◽  
pp. 5992-5997 ◽  
Author(s):  
Omar Elharrouss ◽  
Driss Moujahid ◽  
Hamid Tairi
Keyword(s):  
Motion Detection ◽  
Background Subtraction

Refining background subtraction using consistent motion detection in adverse weather

2019 ◽  
Vol 28 (02) ◽  
pp. 1
Author(s):  
Heechul Jung ◽  
Jeongwoo Ju ◽  
Wonjun Hwang ◽  
Junmo Kim
Keyword(s):  
Motion Detection ◽  
Background Subtraction ◽  
Adverse Weather

scholarly journals Efficient Motion Detection Algorithm in Video Sequences

2014 ◽  
Vol 13 (3) ◽  
pp. 4329-4334 ◽  
Author(s):  
Aree Ali Mohammed
Keyword(s):  
Motion Detection ◽  
Background Subtraction ◽  
Detection Algorithm ◽  
Human Detection ◽  
Video Sequences ◽  
Background Information ◽  
Morphological Operations ◽  
Shadow Removal ◽  
Subtraction Technique ◽  
Objects Detection

Human  motion  analysis  concerns  the detection,  tracking  and  recognition  of  people  behaviors,  from image  sequences  involving  humans. A  reference  frame  is  initially  used  and  considered  as  background  information. While a new object enters into the frame, the foreground information and background information are identified using the reference frame as background model.In this paper, an efficient algorithm is proposed for objects detection in real time video sequences. The method aims at tracking an object like (human) in motion using background subtraction technique. The tracked objects are subject to different pre and post image processing in order to extract the most important features (frame preprocessing is used for shadow removal using morphological operations). They can be used later for recognition in different security system such as (human detection for surveillance). Experimental results show that the proposed method is very efficient in terms of reliability and accuracy of detection.

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