Suspicious Activity Detection using Machine Learning
In security systems anomaly activity detection is one among them. Surveillance cameras have been setup widely for video analysis due to increasing demand for security. Video footages are captured and stored in memory devices. Several places or organization have installed CCTV Cameras for continuous monitoring for surveillance. It is impossible to manually monitor all activities and predict their behavior.
One of the major issue faced by surveillance video analysis is detecting abnormal activity which require additional human efforts. It is laborious task to collect negative samples due to rarity and it is difficult to list all possible negative illustrations.
Suspicious Activity Detection play a vital role in field of research and application system. Detecting real-world problem such as robbery, shoplifting, break-in and other critical events is crucial so that we have to create a deep learning based model to reduce time and manual work. We developed a algorithm that detects suspicious events.
To achieve this this objective we have to follow two approaches.
1. First approach is the model that we have trained.
2. Pre-trained model. To detect the suspicious activity in roads, bank and many other crowded areas, it can be implemented through CCTV Camera and video input to simplify and automate work.
Suspicious Activity Detection using Machine Learning
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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