Inappropriate scene detection in a video stream
Cataloged from PDF version of thesis report.
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| Lenguaje: | English |
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BRAC University
2018
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| Acceso en línea: | http://hdl.handle.net/10361/9469 |
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10361-94692022-01-26T10:04:56Z Inappropriate scene detection in a video stream Tofa, Kamrun Nahar Ahmed, Farhana Shakil, Arif Uddin, Dr. Jia Department of Computer Science and Engineering, BRAC University Video stream Inappropriate scene Scene detection CNNs Cataloged from PDF version of thesis report. Includes bibliographical references (pages 33-35). This thesis report is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. In this paper, we attempted to propose a model to detect inappropriate scenes, such as nudity, weapons, danger, drugs, gore, etc. in any video stream. The detection part is divided into different steps. The very first step of the proposed model is to convert the video stream into its frames. After fragmentation is completed, the image detection algorithms are used on the individual extracted frames to detect our required inappropriate scenes. For the detection part, we decided to fuse three different algorithms to detect the required inappropriate scenes. The nude detection part is handled using two algorithms. The first algorithm would find human figures on the fragmented frames and if found, the human figures would be cropped out and a separate image file would be formed containing the cropped-out part only. Next, the second algorithm would use this cropped image to find the presence of nudity in the original uncropped image. To detect dangerous objects like knives, guns, swords and detect gore, bloody scenes in the fragmented frames, the object detection algorithm is used. For object detection, we used CNNs Object Detection [1,2] algorithm to detect objects and scenes, and for nudity detection, we have used nudepy library from python which is based on detecting skin-colored pixels and identify nudity based on pixel count and its region [18]. After successful detection via the two algorithms, the model is going to give an output to show the percentage of how much of violent scenes and nudity is present in the video sequence. Furthermore, the model will also be able to determine and classify the video as pornography if the percentage nudity detected is over our base scale of what percentage of nudity in a video may be considered as a pornography. Kamrun Nahar Tofa Farhana Ahmed Arif Shakil B. Computer Science and Engineering 2018-02-15T04:12:07Z 2018-02-15T04:12:07Z 2017 12/14/2017 Thesis ID 14101063 ID 14101069 ID 14101031 http://hdl.handle.net/10361/9469 en BRAC University thesis reports 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. 35 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Video stream Inappropriate scene Scene detection CNNs |
| spellingShingle |
Video stream Inappropriate scene Scene detection CNNs Tofa, Kamrun Nahar Ahmed, Farhana Shakil, Arif Inappropriate scene detection in a video stream |
| description |
Cataloged from PDF version of thesis report. |
| author2 |
Uddin, Dr. Jia |
| author_facet |
Uddin, Dr. Jia Tofa, Kamrun Nahar Ahmed, Farhana Shakil, Arif |
| format |
Thesis |
| author |
Tofa, Kamrun Nahar Ahmed, Farhana Shakil, Arif |
| author_sort |
Tofa, Kamrun Nahar |
| title |
Inappropriate scene detection in a video stream |
| title_short |
Inappropriate scene detection in a video stream |
| title_full |
Inappropriate scene detection in a video stream |
| title_fullStr |
Inappropriate scene detection in a video stream |
| title_full_unstemmed |
Inappropriate scene detection in a video stream |
| title_sort |
inappropriate scene detection in a video stream |
| publisher |
BRAC University |
| publishDate |
2018 |
| url |
http://hdl.handle.net/10361/9469 |
| work_keys_str_mv |
AT tofakamrunnahar inappropriatescenedetectioninavideostream AT ahmedfarhana inappropriatescenedetectioninavideostream AT shakilarif inappropriatescenedetectioninavideostream |
| _version_ |
1814307114831577088 |