SFND 2D Feature Tracking
As for this project, you will build the feature tracking part and test various detector / descriptor combinations to see which ones perform best. This project consists of four parts:
- First, focus on loading images, setting up data structures and putting everything into a ring buffer to optimize memory load.
- Then, integrate several keypoint detectors such as HARRIS, FAST, BRISK and SIFT and compare them with regard to number of keypoints and speed.
- In the next part, focus on descriptor extraction and matching using brute force and also the FLANN approach.
- In the last part, once the code framework is complete, test the various algorithms in different combinations and compare them with regard to some performance measures.
Dependencies for Running Locally
- cmake >= 2.8
- All OSes: click here for installation instructions
- make >= 4.1 (Linux, Mac), 3.81 (Windows)
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
- OpenCV >= 4.1
- This must be compiled from source using the
-D OPENCV_ENABLE_NONFREE=ONcmake flag for testing the SIFT and SURF detectors. - The OpenCV 4.1.0 source code can be found here
- This must be compiled from source using the
- gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - install Xcode command line tools
- Windows: recommend using MinGW
Basic Build Instructions
- Clone this repo.
- Make a build directory in the top level directory:
mkdir build && cd build - Compile:
cmake .. && make - Run it:
./2D_feature_tracking.
Results
Keypoints detected
Total number of keypoints in every image for each detector (SHITOMASI, HARRIS, FAST, BRISK, ORB, AKAZE, SIFT)
Corresponding time taken for each detector in every image
Matched Keypoints
Averaged number of matched keypoints for all images using all possible combinations of detectors and descriptors
Corresponding averaged time taken for all possible combinations of detectors and descriptors
Conclusion
To balance the accuracy and speed for each detector / descriptor combination, the ratio of mean-num-of-matched-keypoints to mean-detection-time-taken is used to evaluate the overall performance.
The TOP-3 detector-descriptor combinations are:
| rank | detector | descriptor |
|---|---|---|
| 1 | FAST | BRIEF |
| 2 | FAST | ORB |
| 3 | SHITOMASI | BRIEF |