AsRaNi1/live-cctv
To detect any reasonable change in a live cctv to avoid large storage of data. Once, we notice a change, our goal would be track that object or person causing it. We would be using Computer vision concepts. Our major focus will be on Deep Learning and will try to add as many features in the process.
This project helps operations managers and security personnel efficiently monitor live CCTV feeds, especially from traffic cameras. It processes continuous video input to identify and record only significant changes, such as the appearance or movement of vehicles or people, thereby avoiding the storage of static, uneventful footage. The output is a video showing only relevant events with detected objects highlighted and classified.
No commits in the last 6 months.
Use this if you need to optimize CCTV storage by recording only essential movement and identifying what's causing the change, such as in traffic monitoring or general surveillance.
Not ideal if you require continuous, full-fidelity recording of all video data without any event-based filtering.
Stars
49
Forks
4
Language
Python
License
MIT
Category
Last pushed
May 25, 2022
Commits (30d)
0
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