SFND 2D Feature Tracking

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, you will now build 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, you will focus on loading images, setting up data structures and putting everything into a ring buffer to optimize memory load.
• Then, you will 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, you will then focus on descriptor extraction and matching using brute force and also the FLANN approach we discussed in the previous lesson.
• In the last part, once the code framework is complete, you will test the various algorithms in different combinations and compare them with regard to some performance measures.

See the classroom instruction and code comments for more details on each of these parts. Once you are finished with this project, the keypoint matching part will be set up and you can proceed to the next lesson, where the focus is on integrating Lidar points and on object detection using deep-learning.

UPDATE

Added Benchmarking Script

To easily compare the pair of Keypoint Detector and Descriptor, benchmakr2D.cpp is added. It's very similar to MidTermProject_Camera_Student.cpp file, only the looping and logging are added to save the results as benchmark_data.csv file. Moreover, the results also exported as .pdf and .xlsx files.

Some Observations

It should be mentioned that the results are obtained by using default parameters for the detector and descriptors (by having a better measure for the qualiy one can tune the parameters depending on the application; i.e.: real-time or quality).

AKAZE descriptor is only compatible with its own Keypoint-Detector

SIFT-ORB keypoint detector-descriptor pair with default parameters gave out-of-memory error!

Top3 Detector-Descriptor Combinations

Based on this limited evaluations, one can consider two applications:

1. Real-time:

• FAST-BRIEF: Time-to-Detect-and-Match = 8.44 (ms) | num-Kpts-Matched/Detected = 883/1491 | Kpt-Size-Mean/Std = 7.0/0.0
• FAST-ORB: Time-to-Detect-and-Match = 12.64 (ms) | num-Kpts-Matched/Detected = 859/1491 | Kpt-Size-Mean/Std = 7.0/0.0
• ORB-BRIEF: Time-to-Detect-and-Match = 41.74 (ms) | num-Kpts-Matched/Detected = 450/1161 | Kpt-Size-Mean/Std = 56.06/25.25

2. Quality:

Here, it's not possible to tell which pair produces a better quality (don't have measures such as False-Positive-Rate, ...); but we can say which pair detects and matches the most keypoints + having more diversity in keypoint size (which can indicate that they're extracted from multiple scales and hence more robust to scale change).

• BRISK-SIFT: num-Kpts-Matched/Detected = 1646/2762 | Kpt-Size-Mean/Std = 21.94/14.61 | Time-to-Detect-and-Match = 480.35 (ms)
• BRISK-BRIEF: num-Kpts-Matched/Detected = 1344/2762 | Kpt-Size-Mean/Std = 21.94/14.61 | Time-to-Detect-and-Match = 319.03 (ms)
• BRISK-BRISK: num-Kpts-Matched/Detected = 1298/2762 | Kpt-Size-Mean/Std = 21.94/14.61 | Time-to-Detect-and-Match = 416.53 (ms)

Dependencies for Running Locally

1. cmake >= 2.8

• All OSes: click here for installation instructions

2. 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

3. 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

4. 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.