Motion detection is a fundamental task in computer vision that aims to identify moving objects in a video sequence. The basic idea is to analyze the changes between consecutive frames in order to distinguish foreground objects (moving elements such as vehicles or people) from the background (the static part of the scene). Motion detection is widely used in many real-world applications, including:
- Video surveillance
- Traffic monitoring
- Human activity analysis
- Autonomous driving
- Smart cameras and security systems
- Frame Differencing: Detect motion by computing the difference between frames in the video.
- Background Subtraction: Compare the current frame with a reference background image.
- Adaptive Background Subtraction: Update the background model over time using a running average.
- Mixture of Gaussians (MOG2): A more advanced probabilistic model that represents each pixel as a mixture of Gaussian distributions.
After completing this lab, you are encouraged to work on the Lab 2 Assignment as preparation.
We will go through the assignment during the next lesson, and after the in-class correction, the full solution will be published on Moodle.