Camera based 2D Feature Tracking

The idea is to the feature tracking part and test various detector / descriptor combinations to see which ones perform best. This mid-term project consists of four parts:

• First, 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, descriptor extraction and matching using brute force and also the FLANN approach.
• In the last part, once the code framework is complete, test various algorithms in different combinations and compare them with regard to some performance measures.

Dependencies for Running Locally

Consider using docker image,

$ docker pull ragumanjegowda/docker

For native/host compilation (Note: I have not tested this!)

• 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

Build Instructions

$ mkdir build && cd build
$ cmake -G Ninja .. && ninja
$ ./2D_feature_tracking