Social Distancing Through Image Processing, Video Analysis, and CNN

2021 ◽  
pp. 59-70
Author(s):  
Abdul Bari ◽  
Sharfuddin Waseem Mohammed ◽  
Sameena ◽  
Sharanya
Keyword(s):  
Image Processing ◽  
Video Analysis ◽  
Social Distancing

Post-COVID-19: Deep Image Processing AI to Analyze Social Distancing in a Human Community

2021 ◽  
pp. 59-68
Author(s):  
Francis Class-Peters ◽  
Wilfried Yves Hamilton Adoni ◽  
Tarik Nahhal ◽  
Abdeltif EL Byed ◽  
Moez Krichen ◽  
...  
Keyword(s):  
Image Processing ◽  
Social Distancing ◽  
Human Community ◽  
Deep Image

Processing of Images and Videos for Extracting Text Information from Clustered Features Using Graph Wavelet Transform

2019 ◽  
Vol 16 (2) ◽  
pp. 557-561
Author(s):  
Merlin L. M. Livingston ◽  
Agnel L. G. X. Livingston
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Video Analysis ◽  
Hill Climbing ◽  
Primary Role ◽  
Hill Climbing Algorithm ◽  
Meaningful Information ◽  
Spectral Graph ◽  
Text Information ◽  
Sensitivity Specificity

Image processing is an interesting domain for extracting knowledge from real time video and images for surveillance, automation, robotics, medical and entertainment industries. The data obtained from videos and images are continuous and hold a primary role in semantic based video analysis, retrieval and indexing. When images and videos are obtained from natural and random sources, they need to be processed for identifying text, tracking, binarization and recognising meaningful information for succeeding actions. This proposal defines a solution with assistance of Spectral Graph Wave Transform (SGWT) technique for localizing and extracting text information from images and videos. K Means clustering technique precedes the SGWT process to group features in an image from a quantifying Hill Climbing algorithm. Precision, Sensitivity, Specificity and Accuracy are the four parameters which declares the efficiency of proposed technique. Experimentation is done from training sets from ICDAR and YVT for videos.

scholarly journals Detecção de Roubo de Computadores em Laboratório Usando Visão Computacional

2020 ◽  
Author(s):  
Gabriel Andrade Cordeiro ◽  
Giovani Grockotzki ◽  
Itamar Junior de Azevedo ◽  
João Mantovani ◽  
Matheus Henrique da Silva Santos ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Real Time ◽  
Video Analysis ◽  
Region Of Interest ◽  
Background Information ◽  
Laboratory Monitoring ◽  
Exit Door ◽  
Definition Of ◽  
Computer Labs

Computer theft in computer labs causes academic damage to coursesthat require this resource and ends up directly harming students. Inthis context, this paper describes a methodology applied to detectcomputer removal through video analysis in real-time. For eachframe, image processing and computer vision techniques were used,subtracting background information, binarization, segmentationof the region of interest and definition of contours. The case studywas developed at a Brazilian university. For theft detection, it wasconsidered a black computer tower case carried by people leavingthe laboratory. Monitoring is carried out by a camera positioned infront of the lab exit door. The software developed alerts a suspiciousactivity that may indicate a possible computer theft.

Implementation of Intelligent Video Analysis System and it’s Algorithm Optimization Based on DSP

2011 ◽  
Vol 403-408 ◽  
pp. 217-222
Author(s):  
Hui Wang ◽  
Cheng Wu Liang ◽  
Ying Li
Keyword(s):  
Image Processing ◽  
Video Analysis ◽  
Digital Signal Processor ◽  
Digital Signal ◽  
Optimization Techniques ◽  
Algorithm Optimization ◽  
Automatic Tracking ◽  
Analog Channels ◽  
Analysis System ◽  
Algorithm Level

This paper aims to implement an distributed intelligent video analysis system based on TI multimedia Digital Signal Processor (DSP) TMS320DM642, at the same time, the algorithm optimization is also described. The intelligent video analysis system we proposed provides users with fast and precise video analysis services. The video image is transmitted to the image processing board by analog channels and IP cameras, then the DSP processing the video flow. In this system, target detection, segmentation, feature extraction, alarm and automatic tracking can be implemented. In this system, several optimization techniques are also used in algorithms, including the algorithm level optimization (ALO), the program level optimization (PLO) and the instruction level optimization (ILO). Experimental results show the exellent result in reducing the CPU load.

scholarly journals Social Distancing Detector using YOLO v3 Image Processing Algorithm

2021 ◽  
pp. 783-787
Author(s):  
Ms. Kavita S. Kumavat ◽  
Aman Kumar Sao ◽  
Harish Khedekar ◽  
Chirag Panpaliya ◽  
Shantanu Korde
Keyword(s):  
Image Processing ◽  
Public Awareness ◽  
Image Processing Algorithm ◽  
Social Distancing ◽  
Detection Model ◽  
Model Object ◽  
Learning Framework ◽  
The Social ◽  
Feasible Option ◽  
Bounding Boxes

The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.

scholarly journals Social Distancing Detector using Image Processing

2021 ◽  
pp. 542-545
Author(s):  
Aman Kumar Sao ◽  
Harish Khedekar ◽  
Chirag Panpaliya ◽  
Shantanu Korde ◽  
Ms. Kavita S. Kumavat
Keyword(s):  
Image Processing ◽  
Public Awareness ◽  
Social Distancing ◽  
Detection Model ◽  
Model Object ◽  
Learning Framework ◽  
The Social ◽  
The World ◽  
Feasible Option ◽  
Bounding Boxes

The lack of public awareness and negligence, the pandemic due to coronavirus(covid19) has brought a global crisis with its deadly spread to more than 180 countries, and about 147 million confirmed cases along with 3.11 million deaths globally as of 26th April 2021. Due to the absence of the vaccine against the covid19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. And this notion motivated us to bring up the idea of a social distancing detector using image processing which includes a deep learning framework for automation task monitoring. The framework utilizes the YOLO v3 model object detection model to separate moving people from the background and to detect people by using bounding boxes. The basic idea of this article is to analyze the social distancing violation index rate that how many people violate the rule of social distancing in a particular interval of time.

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