Dynamic image analysis for abnormal behavior detection
Cataloged from PDF version of thesis report.
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| भाषा: | English |
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BRAC University
2017
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| ऑनलाइन पहुंच: | http://hdl.handle.net/10361/8107 |
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10361-81072022-01-26T10:15:59Z Dynamic image analysis for abnormal behavior detection Ahmed, Md. Ashik Isha, Mushfique Ahmed Ahmed, Al-Amin Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, BRAC University Dynamic image Abnormal behavior Human posture Object identity verification Deep learning Convolutional neural network Cataloged from PDF version of thesis report. Includes bibliographical references (page 41-42). This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2017. Our world is now in such developing state where security is more of a concern rather than privacy for an individual. Nowadays, abnormal behavior detection system plays a very important role in various sectors such as, security, prison, bank etc. Abnormal behavior and its definition is different in many cases. In the vivid sense the definition of abnormal behavior, it is something deviating from the normal or differing from the typical scenario. Moreover, this abnormal behavior detection refers to the problem of finding patterns in data that do not conform to expected behavior. For a particular domain abnormal behavior can be different from the classic definition of abnormality. Detection of abnormal behavior is an important area of research in computer vision and is also driven by a wide application domains, such as dynamic image analysis from a video surveillance. Convolutional neural network made this detection and classification way easier and efficient. In this project we are prompted to detect abnormal or suspicious behavior by an individual person. Our purpose is to detect behavior which is not normal from dynamic images taken from a video surveillance. In this case we are using Convolutional Neural Network (CNN) to detect abnormal behavior. In experiments, our proposed system detected the behavior of individuals in normal scenario successfully with the accuracy of 98%. Moreover, it also detects any deviations from previous data for any new scenario from different dynamic images. Our system can be implemented in advanced security purposes. Md. Ashik Ahmed Mushfique Ahmed Isha Al-Amin Ahmed B. Computer Science and Engineering 2017-05-08T09:09:04Z 2017-05-08T09:09:04Z 2017 4/18/2017 Thesis ID 12201009 ID 12201077 ID 12201102 http://hdl.handle.net/10361/8107 en BRAC University thesis 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. 42 pages application/pdf BRAC University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Dynamic image Abnormal behavior Human posture Object identity verification Deep learning Convolutional neural network |
| spellingShingle |
Dynamic image Abnormal behavior Human posture Object identity verification Deep learning Convolutional neural network Ahmed, Md. Ashik Isha, Mushfique Ahmed Ahmed, Al-Amin Dynamic image analysis for abnormal behavior detection |
| description |
Cataloged from PDF version of thesis report. |
| author2 |
Chakrabarty, Dr. Amitabha |
| author_facet |
Chakrabarty, Dr. Amitabha Ahmed, Md. Ashik Isha, Mushfique Ahmed Ahmed, Al-Amin |
| format |
Thesis |
| author |
Ahmed, Md. Ashik Isha, Mushfique Ahmed Ahmed, Al-Amin |
| author_sort |
Ahmed, Md. Ashik |
| title |
Dynamic image analysis for abnormal behavior detection |
| title_short |
Dynamic image analysis for abnormal behavior detection |
| title_full |
Dynamic image analysis for abnormal behavior detection |
| title_fullStr |
Dynamic image analysis for abnormal behavior detection |
| title_full_unstemmed |
Dynamic image analysis for abnormal behavior detection |
| title_sort |
dynamic image analysis for abnormal behavior detection |
| publisher |
BRAC University |
| publishDate |
2017 |
| url |
http://hdl.handle.net/10361/8107 |
| work_keys_str_mv |
AT ahmedmdashik dynamicimageanalysisforabnormalbehaviordetection AT ishamushfiqueahmed dynamicimageanalysisforabnormalbehaviordetection AT ahmedalamin dynamicimageanalysisforabnormalbehaviordetection |
| _version_ |
1814308598701883392 |