Download Free Project Object Tracking Using Python and OpenCV source code and documentation with complete guidance. OpenCV is an Open Source Commuter Vision Library that has interfaces for C++ , Python and Java, and supports Windows, Macos, Mac OS, iOS , and Android. So it can quickly be installed with Python and Linux framework in Raspberry Pi. And Raspberry Pi can be used with OpenCV and connected camera to build several applications for real-time image processing, such as face recognition , face lock, object monitoring, car number plate recognition, home surveillance device etc.
Object tracking is a captivating field that has gained significant momentum in computer vision and image processing, thanks to Python and OpenCV. This technology allows us to observe and follow the movements of objects in videos, which has numerous real-world applications such as surveillance, autonomous vehicles, robotics, and sports analysis.
Python is a versatile programming language that can be coupled with the OpenCV library to offer a wide range of computer vision tools, making object tracking more efficient and accessible. The OpenCV library comes with a plethora of pre-built functions and algorithms for tracking objects, while Python’s simplicity and readability make it easier for developers to understand the concepts involved quickly.
Object Tracking Using Python and OpenCV
Face identification and recognition are the most relevant machine vision use case, so they are used to do amazing tasks including
- Self Awareness
- Facial recognition
- Situation labelling
- Robotics Navigation
- Auto-driving vehicles
- Recognition for Handwriting
- The prevention of infections and cancer
- Identifying features in pictures on satellite