Crowd Real Time Video Classification, Count and Flow
The need for smart surveillance systems is ever growing in the present days, involved in purposes such as security and marketing to track the movements of different classes of people. Our project in computer vision with deep learning is focussed on segregating the gender composition of people, wh...
Saved in:
Main Author: | |
---|---|
Format: | Final Year Project |
Language: | English |
Published: |
Universiti Teknologi PETRONAS
2020
|
Subjects: | |
Online Access: | http://utpedia.utp.edu.my/23053/1/FYP2%20Dissertation.pdf http://utpedia.utp.edu.my/23053/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my-utp-utpedia.23053 |
---|---|
record_format |
eprints |
spelling |
my-utp-utpedia.230532022-03-11T04:28:45Z http://utpedia.utp.edu.my/23053/ Crowd Real Time Video Classification, Count and Flow Wee, Joel Hong Shen TK Electrical engineering. Electronics Nuclear engineering The need for smart surveillance systems is ever growing in the present days, involved in purposes such as security and marketing to track the movements of different classes of people. Our project in computer vision with deep learning is focussed on segregating the gender composition of people, while recognising and counting their flow of direction. The project will be used with reference to real-time video processing. The challenges/problem statement for the project is the lack of definitive methods to determine the direction of individuals, computationally expensive object detection models and lack of practical gender detection datasets. In this paper, the method of object detection with object tracking running in parallel is suggested to improve processing time of video frames, with a usage of OpenCV to identify existing, new and out-of-frame objects. A practical dataset of genders from crowd view to be used to fine-tune a pretrained object detection model is suggested for application as well. Universiti Teknologi PETRONAS 2020-09 Final Year Project NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/23053/1/FYP2%20Dissertation.pdf Wee, Joel Hong Shen (2020) Crowd Real Time Video Classification, Count and Flow. Universiti Teknologi PETRONAS. (Submitted) |
institution |
Universiti Teknologi Petronas |
building |
UTP Resource Centre |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Petronas |
content_source |
UTP Electronic and Digitized Intellectual Asset |
url_provider |
http://utpedia.utp.edu.my/ |
language |
English |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Wee, Joel Hong Shen Crowd Real Time Video Classification, Count and Flow |
description |
The need for smart surveillance systems is ever growing in the present days,
involved in purposes such as security and marketing to track the movements of
different classes of people. Our project in computer vision with deep learning is
focussed on segregating the gender composition of people, while recognising and
counting their flow of direction. The project will be used with reference to real-time
video processing. The challenges/problem statement for the project is the lack of
definitive methods to determine the direction of individuals, computationally
expensive object detection models and lack of practical gender detection datasets. In
this paper, the method of object detection with object tracking running in parallel is
suggested to improve processing time of video frames, with a usage of OpenCV to
identify existing, new and out-of-frame objects. A practical dataset of genders from
crowd view to be used to fine-tune a pretrained object detection model is suggested
for application as well. |
format |
Final Year Project |
author |
Wee, Joel Hong Shen |
author_facet |
Wee, Joel Hong Shen |
author_sort |
Wee, Joel Hong Shen |
title |
Crowd Real Time Video Classification, Count and Flow |
title_short |
Crowd Real Time Video Classification, Count and Flow |
title_full |
Crowd Real Time Video Classification, Count and Flow |
title_fullStr |
Crowd Real Time Video Classification, Count and Flow |
title_full_unstemmed |
Crowd Real Time Video Classification, Count and Flow |
title_sort |
crowd real time video classification, count and flow |
publisher |
Universiti Teknologi PETRONAS |
publishDate |
2020 |
url |
http://utpedia.utp.edu.my/23053/1/FYP2%20Dissertation.pdf http://utpedia.utp.edu.my/23053/ |
_version_ |
1739833025418493952 |
score |
13.15806 |