scholarly journals WisenetMD: Motion Detection Using Dynamic Background Region Analysis

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 621 ◽  
Author(s):  
Sang-ha Lee ◽  
Gyu-cheol Lee ◽  
Jisang Yoo ◽  
Soonchul Kwon
Keyword(s):  
Motion Detection ◽  
Processing Speed ◽  
Background Subtraction ◽  
Median Filter ◽  
False Positives ◽  
Subtraction Method ◽  
Robust Performance ◽  
Background Region ◽  
Dynamic Background ◽  
F Measure

In this paper, we propose a method for calculating the dynamic background region in a video and removing false positives in order to overcome the problems of false positives that occur due to the dynamic background and frame drop at slow speeds. Therefore, we need an efficient algorithm with a robust performance value including processing speed. The foreground is separated from the background by comparing the similarities between false positives and the foreground. In order to improve the processing speed, the median filter was optimized for the binary image. The proposed method was based on a CDnet 2012/2014 dataset and we achieved precision of 76.68%, FPR of 0.90%, FNR of 18.02%, and an F-measure of 75.35%. The average ranking across categories is 14.36, which is superior to the background subtraction method. The proposed method was operated at 45 fps (CPU), 150 fps (GPU) at 320 × 240 resolution. Therefore, we expect that the proposed method can be applied to current commercialized CCTV without any hardware upgrades.

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 An effective approach for video condensation by ribbon carving

2018 ◽  
Vol 7 (2.21) ◽  
pp. 427
Author(s):  
M Latha ◽  
U V. Anbazhagu ◽  
. .
Keyword(s):  
Processing Speed ◽  
Background Subtraction ◽  
Median Filter ◽  
Memory Allocation ◽  
Seam Carving ◽  
Time Intervals ◽  
Kernel Model ◽  
Temporal Direction ◽  
Non Parametric

The proposed system aims in developing a method to stream video based on time intervals. Regular or irregular sub-sampling is not sufficient for scaling of video in time. Seam carving has been used where an Image can be resized either by inserting or removing pixels. Ribbon carving is an extension of seam carving which resizes the video in temporal direction. The non-parametric kernel model used for background subtraction has been replaced with temporal median filter. It enhances the processing speed and memory allocation has been more efficient, thus reducing the time of video without loss of data.  

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.

A novel background subtraction method based on color invariants

2013 ◽  
Vol 117 (11) ◽  
pp. 1589-1597 ◽  
Author(s):  
Hong Zhou ◽  
Yiru Chen ◽  
Rong Feng
Keyword(s):  
Background Subtraction ◽  
Subtraction Method
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