Crowd Density Estimation using Deep Learning
Crowd Counting and Density Based Estimation refers to counting the number of people with density estimation present in certain area. Day to day population density increases and it does complexity of the calculations. Advancement in Deep Convolution Neural Network (CNN) and it largely depend on dataset. The population is increasing an exponential rate. 72 million people currently increases annually. Movement of people whether crowds or pedestrians seems to improperly controlled. Political Rallies, Music Concerts, Sporting Events are all occasions where large number of people congregate. For Security and Safety of big groups of people, good management is required, installation of CCTV for several purposes, monitoring public spaces or traffic management, crowd counting, anomaly detection. Death also seems in high density crowd area. mob crushes, public panic and breakdown of control can contribute a big crowd tragedy and large number of people may death in these types of accidents. This type of accident can be avoided by detecting an uncontrolled crowd flow. The academic community has put a lot of efforts into establishing several framework for automatic crowd counting in video surveillance and people counting in extremely crowded photos, a large dataset is essential.
Crowd density estimation Computer Vision
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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