Crowd Counting and Density Estimation using Machine Learning | Crowd Counting Project
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.
A density map contains geographical data that shows overall number of people in picture. Deep learning is widely used to solve crowd counting and has achieved great progress as a result of abilities to accurately simulate and variance in crowd densities between locations.
Project Functionality of Crowd Counting using Machine Learning:-
1.This project is developed using Python Programming Language and Tkinter is used for creating Graphical User Interface.
2. User can gave input of image, video and live streaming option and predict output.
3. For Input Image option, user have to gave input image and model will predict and gave output with Number of People count and including density.
4. In Input Video, user have to pass video to model and model will predict counting of people and density estimation.
5. Live Streaming:- User have to gave IP Address and it model will take access of camera and it will predict live people counting with density estimation.
Crowd Density Estimation with Convolutional Neural Networks
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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