2D Feature Tracking (Sensor Fusion Nanodegree)

The idea of the camera course is to build a collision detection system - that's the overall goal for the Final Project.

As a preparation for this, I built the feature tracking part and tested various detector / descriptor combinations to see which ones perform best. This mid-term project consists of four parts:

• First, focusing on loading images, setting up data structures and putting everything into a ring buffer to optimize memory load.
• Then, integrating several keypoint detectors such as HARRIS, FAST, BRISK and SIFT and comparing them with regard to number of keypoints and speed.
• Focusing on descriptor extraction and matching using brute force and also the FLANN approach that was discussed in the previous lesson.
• In the last part, testing the various algorithms in different combinations and comparing them with regard to some performance measures.

Findings

Here are my findings:

Demo

Here is a GIF showing the project during runtime:

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.