View All Documents & Correspondence
Deep Learning Model To Analyze The Attentiveness Of Crowd Using Pose Estimation, Person And Eye Detection
Abstract: Analysis of Human Behaviour has attracted many research attention in Computer Vision. Various applications of Computer Vision such as, education, health care, human-computer interactions and video understanding. Though many research work in this domain, the problems and challenges have remained unsolved. Some of the challenges are Occlusion, Eye Movement Metrics, Interclass variation, etc., In the part of large group, our project aims to extract behavioural information from the input videos or live streams which contains input as Crowd Environment. This method is applied in Classroom Environment to enhance the teaching quality by analyzing the student activity. Many studies have focused on the physical activity of a student such as hand raising gestures and sleeping activity by Pose Estimation and Person Detection. So, we are proposing Oculus Behaviour to improve and enhance the accuracy of the existing system. Therefore, this project proposes a Deep Learning Model to analyse the attentiveness of Crowd using Pose Estimation, Person and Eye Detection.
Notices, Deadlines & Correspondence
Patent Information
Applicants
Monika R
Inventors
1. Monika R
2. Parveen H
3. Sathish M
4. Dr. Muthu Senthil B
Specification
Description:The system was tested on some students in a classroom to analyze
the impact on the detections. The detections like Skeleton Pose Estimation,
Person Detection and Oculus Behaviour were surveyed before and after
usage of the system. [1]
The survey shows results like following
The pose estimation detects the skeleton structure of each students in
the classroom. [2]
These estimation also detects the activity and position of each
students in the classroom.
It shows the activity such as Hands Up and Hands Down and also
the positions such as Standing and Sitting.
The Person Detection detects the Students in the classroom and gives
the total number of students present in the classroom. [3]
The Oculus Behaviour detects the eyes and iris of a single student in
the Classroom.
By detecting the eyes and iris, it gives the blink count and position of
the iris.
Using this Oculus detection, we can predict the activity of a person.
[4]
, Claims:1. Dell Inspiron System 3567 is to be the claim 3.
2. Intel Core i5 7200u is used for processing of claim 3.b.
3. DDR44 RAM (8 GB) can be used as a storage for detection is claim
4. Dell Integrated Webcam is used as a video camera that receives
videos as frames from claim 1 & 2.
5. PyCharm is used as a platform to run the detections to be the claim
Documents
Application Documents
| # | Name | Date |
|---|---|---|
| 1 | 202241027965-FER.pdf | 2022-09-07 |
| 1 | 202241027965-STATEMENT OF UNDERTAKING (FORM 3) [16-05-2022(online)].pdf | 2022-05-16 |
| 2 | 202241027965-COMPLETE SPECIFICATION [16-05-2022(online)].pdf | 2022-05-16 |
| 2 | 202241027965-REQUEST FOR EXAMINATION (FORM-18) [16-05-2022(online)].pdf | 2022-05-16 |
| 3 | 202241027965-DRAWINGS [16-05-2022(online)].pdf | 2022-05-16 |
| 3 | 202241027965-REQUEST FOR EARLY PUBLICATION(FORM-9) [16-05-2022(online)].pdf | 2022-05-16 |
| 4 | 202241027965-FORM 1 [16-05-2022(online)].pdf | 2022-05-16 |
| 5 | 202241027965-DRAWINGS [16-05-2022(online)].pdf | 2022-05-16 |
| 5 | 202241027965-REQUEST FOR EARLY PUBLICATION(FORM-9) [16-05-2022(online)].pdf | 2022-05-16 |
| 6 | 202241027965-COMPLETE SPECIFICATION [16-05-2022(online)].pdf | 2022-05-16 |
| 6 | 202241027965-REQUEST FOR EXAMINATION (FORM-18) [16-05-2022(online)].pdf | 2022-05-16 |
| 7 | 202241027965-FER.pdf | 2022-09-07 |
| 7 | 202241027965-STATEMENT OF UNDERTAKING (FORM 3) [16-05-2022(online)].pdf | 2022-05-16 |
Search Strategy
| 1 | SearchStrategyE_06-09-2022.pdf |