Person Crowded Detection using Python
People Counting is a technique to count the number of people or objects in particular picture. There are many use cases for counting the number of people from crowd image or video, like if there is covid disease spread so we can stop from large crowd gathering from any particular place, it shows the usefulness of people counting in real life. Crowd counting is not an easy task, or if it is done manually with our hands then we will lose count at the mid this manual task is Laboure. To solve this problem, we have created an ML model that can detect the person from image and count. And in the output image it will provide the total number of people in that particular image. We develop the model using Convolutional Neural Network (CNN) technique. That particular system that we make is capable to count pedestrians in a mall.
Step 1: We the following data sources: 🔗 https://universe.roboflow.com/sdd-hlrl5/all_av-xbg1a/dataset/2 📷 A collection of “point cloud of person” images from Google. Additionally, you will need to provide some images for better accuracy. I will also collect images from my end.
Step 2: We label all images as needed for detection.
Step 3: We use the Ultralytics YOLO library to train the YOLOv8 algorithm.
Step 4: The best-trained model, which provides the highest classification and detection accuracy, will be stored.
Step 5: A UI will be implemented using Tkinter.
Step 6: The UI will have three input sources: 📸 Image Module 🎥 Video Module 📹 Camera Module
Step 7: For UI-based predictions, you will need to select an input source, and the system will make predictions using the YOLO-trained algorithm.
People Counting Using 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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