This project implements a human detection system using the MobileNet SSD model. The code processes video streams or images to detect humans in real-time, utilizing several modular components such as preprocessing, detection, and postprocessing.
The Human Detection project is designed to detect humans in video streams using a MobileNet SSD model. The detection process involves reading frames from a video stream, detecting human presence, and drawing bounding boxes around detected individuals. The processed video is then saved to an output file.
- Human Detection: Detects humans in video streams using the MobileNet SSD model.
- Real-time Processing: Processes video frames in real-time.
- Modular Design: Uses separate modules for preprocessing, detection, and postprocessing.
- Configurable Input: Supports both video file and live stream input.
To run this project, ensure you have the following dependencies installed:
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Clone the repository:
git clone https://github.com/MahnazRazavi/Realtime-human-detection.git cd Realtime-human-detection -
Install required packages:
Ensure you have Python 3.6 or higher and install the required packages:
pip install opencv-python