Practical Assignment 2

Please put your name (or names if you work in a group) here:
Name: Melvin Wolf and Osama-al-Wardi

Problem 2.1

Feature Extraction (Points 30)

1. Fork the current repository
2. Read the OpenCV documentation about the Good-Features-to-Track: Shi-Tomasi Corner Detector
3. Implement the feature detection and extraction using the cv.goodFeaturesToTrack() method. Detect features every 300-th frame and draw them on every frame from web-camera. Please extract between 50 and 200 features. (We extract 50)

Problem 2.2

Sparse Optical Frlow Estimation (Points 50)

1. Read the OpenCV documentation about the Lukas-Kanade method: Lucas-Kanade Optical Flow
2. Implement optical flow estimation using the cv.calcOpticalFlowPyrLK() method. Calculate Optical Flow between every subsequent 2 frames from web-camera using the features achieved in Problem 2.1.
3. The features are newly detected every 300th frames, i.e. approximately once in every 10 seconds. In between the features location at the image will not change.

Problem 2.3

Optical Flow Visualization (Points 20)

1. Visualize the Optical flow, using the displacement vectors calculated in Problem 2.2. For this use the OpenCV drawing functions.
2. Now please move the detected features from Problem 2.1 by the displacement vectors alculated in Problem 2.2 and draw them with the new position in every frame from web-camera. If this is implemented correctly, the features will move with the moving objects in your video stream.

Note

Please do not copy-paste the example code from the OpenCV documentation, but try to understand the example code and implement the solution to the problem by yourself.