Muhammad Abdullah
FAST
· 2021
·
17I - 0038
Email
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Phone
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LinkedIn
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GitHub
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Academic
Program
BSCS
CGPA
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Year
2021
Education
SEECS
Address
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DOB
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Career
Current role
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Target role
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Skills
Pytorch, OpenCV, Python 3, Django, Android Studio Java
Verbatim text
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Threatify – Threat Detection Using CCTV Security is a huge concern in the modern world, since crime is increasing. To prevent crime, CCTVs are installed everywhere along with CCTV operators to monitor the screens. This, however, isn’t feasible at homes & shops because we cannot have an operator watching the footage 24/7. Having CCTVs but still being unable to report crime either not viewing it at that instance or being under the influence of gunmen could cause lots of damage. We plan to develop a deep learning-based system which is capable of detecting threats the moment it occurs and report the crime to the corresponding authorities as fast as possible. This could result in minimizing the response time resulting in less damage, such techniques would have high demand in this digital world. Features include: ● Threat detection using CCTV feed. ● Threat classification. (Arson & Explosion, Fighting, Gun-Event) ● Web application. ● Android Mobile App Notifying and sending location to the client and authorities about the threat.
AI enrichment
Muhammad Abdullah holds a BSCS degree and has developed a deep learning-based threat detection system utilizing CCTV feeds for real-time crime monitoring. The project includes features for threat classification, such as arson and fighting, alongside web and Android mobile applications for instant notifications.
Skills (AI)
["Deep Learning", "Computer Vision", "Threat Detection", "Web Application Development", "Android Development", "CCTV Integration"]
Status: ai_done
Provenance
Source file: Graduate Directory FAST School of Computing 2021 (1st Final) (1).pdfFrom job #24 page 238
Created: 1778170890