Real time action recognition from video footage
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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| Формат: | Диссертация |
| Язык: | English |
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Brac University
2021
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| Online-ссылка: | http://hdl.handle.net/10361/14967 |
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10361-149672022-01-26T10:18:21Z Real time action recognition from video footage Apon, Tasnim Sakib Chowdhury, Mushfiqul Islam Reza, MD Zubair Datta, Arpita Hasan, Syeda Tanjina Department of Computer Science and Engineering, Brac University Deep Neural Network Deep learning Real Time Action Action Detection from Footage Crime Detection from Footage Surveillance action detection Deep Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 48). Crime rate is increasing proportionally with the increasing rate of the population. The most prominent approach was to introduce Closed-Circuit Television (CCTV) camera-based surveillance to tackle the issue. Video surveillance cameras have added a new dimension to detect crime. Several research works on autonomous security camera surveillance are currently ongoing, where the fundamental goal is to discover violent activity from video feeds. From the technical viewpoint, this is a challenging problem because analyzing a set of frames, i.e., videos in temporal dimension to detect violence might need careful machine learning model training to reduce false results. This research focused on this problem by integrating state-of-the-art Deep Learning methods to ensure a robust pipeline for autonomous surveillance for detecting violent activities, e.g., kicking, punching, and slapping. Initially, we designed a dataset of this specific interest, which were 600 videos (200 for each action). Later, we have utilized existing pre-trained model architectures to extract features, followed by classification and accuracy analysis.Also, We have classified our models’ accuracy, confusion matrix on different pre-trained architectures like VGG16, InceptionV3, ResNet50 and MobileNet V2. Among the pre-trained models VGG16 and MobileNet V2 performed better. Tasnim Sakib Apon Mushfiqul Islam Chowdhury MD Zubair Reza Arpita Datta Syeda Tanjina Hasan B. Computer Science 2021-09-03T12:37:44Z 2021-09-03T12:37:44Z 2021 2021 Thesis ID 20241068 ID 17101120 ID 17101275 ID 18341008 ID 17101184 http://hdl.handle.net/10361/14967 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 48 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep Neural Network Deep learning Real Time Action Action Detection from Footage Crime Detection from Footage Surveillance action detection Deep Learning |
| spellingShingle |
Deep Neural Network Deep learning Real Time Action Action Detection from Footage Crime Detection from Footage Surveillance action detection Deep Learning Apon, Tasnim Sakib Chowdhury, Mushfiqul Islam Reza, MD Zubair Datta, Arpita Hasan, Syeda Tanjina Real time action recognition from video footage |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Department of Computer Science and Engineering, Brac University |
| author_facet |
Department of Computer Science and Engineering, Brac University Apon, Tasnim Sakib Chowdhury, Mushfiqul Islam Reza, MD Zubair Datta, Arpita Hasan, Syeda Tanjina |
| format |
Thesis |
| author |
Apon, Tasnim Sakib Chowdhury, Mushfiqul Islam Reza, MD Zubair Datta, Arpita Hasan, Syeda Tanjina |
| author_sort |
Apon, Tasnim Sakib |
| title |
Real time action recognition from video footage |
| title_short |
Real time action recognition from video footage |
| title_full |
Real time action recognition from video footage |
| title_fullStr |
Real time action recognition from video footage |
| title_full_unstemmed |
Real time action recognition from video footage |
| title_sort |
real time action recognition from video footage |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/14967 |
| work_keys_str_mv |
AT apontasnimsakib realtimeactionrecognitionfromvideofootage AT chowdhurymushfiqulislam realtimeactionrecognitionfromvideofootage AT rezamdzubair realtimeactionrecognitionfromvideofootage AT dattaarpita realtimeactionrecognitionfromvideofootage AT hasansyedatanjina realtimeactionrecognitionfromvideofootage |
| _version_ |
1814308815997239296 |