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...

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Main Author: Wee, Joel Hong Shen
Format: Final Year Project
Language: English
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-utpedia.23053 /
Published: Universiti Teknologi PETRONAS 2020
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access: http://utpedia.utp.edu.my/23053/1/FYP2%20Dissertation.pdf
http://utpedia.utp.edu.my/23053/
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id utp-utpedia.23053
recordtype eprints
spelling 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
collection UTPedia
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_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_ 1741195900745678848
score 11.62408

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