• 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
• Git LFS
  • Weight files are handled using LFS
• 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

1. Clone this repo.
2. Make a build directory in the top level project directory: mkdir build && cd build
3. Compile: cmake .. && make
4. Run it: ./3D_object_tracking. (If you have run-time error, you may need to re-download the yolov3.weights. just run wget "https://pjreddie.com/media/files/yolov3.weights", and copy yolov3.weights into dat/yolo/)

The matchBoundingBoxes functon is implemented. I take one matched keypoint at a time find the corresponsing point in current and prev frame, then I check which bounding box in prev and curr frame the point belong to, once found store the value and increment the count check line 283-332 in camFusion_student.cpp.

First, I find closest distance to Lidar points within ego lane, then I use the equation that I learned from the TTC section to compute the TTC. check line 238-280 in camFusion_student.cpp.

First, I take one match pair, then if the previous keypoint and current keypoint are within the same bounding box, then we add this match to a vector. After finishing the loop, I assign the obtained vector to boundingBox.kptMatches. Then I filter out the outliers based on their distance. check line 145-178 in camFusion_student.cpp.

This section is heavily based on the previous practice code. First I compute distance ratios between all matched keypoints, then I use median of computed distance ratio, and apply the equation I learned to compute TTC. check line 182-235 in camFusion_student.cpp.

Based on the given sequence, I think the faulty measurement is at frame 4. This is because there are points located on the other object rather than the car that is at front.

1. The detector/descriptor combination has been listed here. From this spread sheet, we know FAST/BRIEF combination is the best because it is the most efficient combination and the TTC is reasonalbly good.
2. From the image sequcene provided, the faulty measurement is at frame 5. I think this is due to too many mismatched keypoints.