2D Feature Tracking

This project focusses on building the feature tracking part and test various detector/descriptor combinations to see which ones perform best. It consists of four parts:

• First, we will focus on loading images, setting up data structures, and putting everything into a ring buffer to optimize memory load.
• Then, we will integrate several keypoint detectors - SHI-TOMASI, HARRIS, FAST, BRISK, ORB, AKAZE, and SIFT and compare them concerning the number of keypoints and speed.
• In the next part, we will then focus on descriptor extraction and matching using brute force and also the FLANN approach.
• In the last part, once the code framework is complete, we will test the various algorithms in different combinations and compare them about 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=ON cmake flag for testing the SIFT and SURF detectors.
  • The OpenCV 4.1.0 source code can be found here
• 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

1. Clone this repo.
2. Make a build directory in the top level directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./2D_feature_tracking.