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Camera-Based-2D-Feature-Tracking

This mini-project is an exhaustive comparison between different keypoint detectors and descriptors present in C++ OpenCV library. The matching is performed on a suquence from KITTI DATASET. Below are the results and observation from my implementation.

The starter code is taken from Udacity Sensor Fusion github.

Processor and Dataset Information

Parameter Value
PROCESSOR LAPTOP INTEL I9 2023
DATASET SEQUENCE KITTI/2011_09_26/IMAGE_00/DATA/000000
C++ OPENCV VERSION 4.1.2

Key Points Detected

DETECTOR AVG NO KEY POINTS 1 2 3 4 5 6 7 8 9 10
HARRIS 24.8 17 14 18 21 26 43 18 31 26 34
SHI-TOMASI 117.9 125 118 123 120 120 113 114 123 111 112
FAST 149.1 149 152 150 155 149 149 156 150 138 143
BRISK 276.2 264 282 282 277 297 279 289 272 266 254
ORB 116.1 92 102 106 113 109 125 130 129 127 128
AKAZE 167 166 157 161 155 163 164 173 175 177 179
SIFT 138.6 138 132 124 137 134 140 137 148 159 137

Key Points Matched (AVG)

BRISK BRIEF ORB FREAK AKAZE SIFT
HARRIS 21.4 21.4 21.4 21.4 N/A N/A
SHI-TOMASI 106.7 106.7 106.7 106.7 N/A N/A
FAST 134 134 134 134 N/A N/A
BRISK 250 250 250 232 N/A N/A
ORB 95 103 103 54 N/A N/A
AKAZE 149.1 149.1 149.1 149.1 149.1 N/A
SIFT 124.9 124.8 N/A 123.9 N/A 124.9

Time Detection (AVG)

BRISK BRIEF ORB FREAK AKAZE SIFT
HARRIS 8.312 9.18 8.909 7.79 N/A N/A
SHI-TOMASI 8.152 8.9 13.5 7.7 N/A N/A
FAST 0.6 0.71 0.68 0.64 N/A N/A
BRISK 24.88 25.38 24.96 24.899 N/A N/A
ORB 4.2 4.49 6.18 4.89 N/A N/A
AKAZE 36.13 49.9 48 37.8 47 N/A
SIFT 48 55 N/A 41 N/A 42

Time Description Extraction (AVG)

BRISK BRIEF ORB FREAK AKAZE SIFT
HARRIS 0.626 0.394 0.452 20.6 N/A N/A
SHI-TOMASI 1.16 0.67 1.12 20 N/A N/A
FAST 1.131 0.85 0.66 21.1665 N/A N/A
BRISK 1.7 0.572 2.28 21.07 N/A N/A
ORB 0.809 0.394 3.81 20.64 N/A N/A
AKAZE 1.16 0.63 2.69 23.2 32.7 N/A
SIFT 0.96 0.74 N/A 21 N/A 34

Observations

HARRIS: Scattered points. Relatively less than others. SHI-TOMASI: Points more present on the windshield area and few on the edge of the bumper. FAST: Windshield area more scattered than Shi Tomasi but overall similar. More points are outside the car. BRISK: Distribution is similar to Shi Tomasi and Fast. ORB: More points on the right side of the windshield. No points on the bumper. AKAZE: Points are outside the car but it has detected points on the car as well. SIFT: Very similar to SHI-TOMASI more points on the edge of the car.

According to my results. Top 3 combination.

  1. FAST Detector + BRIEF Desc extractor
  2. FAST Detector + BRISK Desc extractor
  3. ORB Detector + BRIEF Desc extractor

Dependencies for Running Locally

  1. cmake >= 2.8
  1. make >= 4.1 (Linux, Mac), 3.81 (Windows)
  1. OpenCV >= 4.1
  • All OSes: refer to the official instructions
  • This must be compiled from source using the -D OPENCV_ENABLE_NONFREE=ON cmake flag for testing the SIFT and SURF detectors. If using homebrew: $> brew install --build-from-source opencv will install required dependencies and compile opencv with the opencv_contrib module by default (no need to set -DOPENCV_ENABLE_NONFREE=ON manually).
  • The OpenCV 4.1.0 source code can be found here
  1. 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 either MinGW-w64 or Microsoft's VCPKG, a C++ package manager. VCPKG maintains its own binary distributions of OpenCV and many other packages. To see what packages are available, type vcpkg search at the command prompt. For example, once you've VCPKG installed, you can install OpenCV 4.1 with the command:
c:\vcpkg> vcpkg install opencv4[nonfree,contrib]:x64-windows

Then, add C:\vcpkg\installed\x64-windows\bin and C:\vcpkg\installed\x64-windows\debug\bin to your user's PATH variable. Also, set the CMake Toolchain File to c:\vcpkg\scripts\buildsystems\vcpkg.cmake.

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.