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.

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.

Writeup

MP.1 Data Buffer Optimization

The "ring buffer" is naively implemented by pushing new frames onto the dataBuffer until the size is reached. Then the buffer size is reduced by erasing the oldest frame.

MP.2 Keypoint Detection

The HARRIS, FAST, BRISK, ORB, AKAZE, and SIFT keypoint detectors were implemented. The methods were made selectable by changing the string's definition.

MP.3 Keypoint Removal

The .contains method of the VehicleRect object is used to detect and remove keypoints outside of the rectangle.

MP.4 Keypoint Descriptors

The BRIEF, ORB, FREAK, AKAZE and SIFT descriptors were implemented. The methods were made selectable by changing the string's definition.

MP.5 Descriptor Matching

The FLANN matcher and kNN matchers are implemented. The methods were made selectable by changing the string's definition

MP.6 Descriptor Distance Ratio

Lowe's distance ratio test was implemented by comparing the best and second best matches to decide if a pair of matched keypoints should be stored.

MP.7 Performance Evaluation 1

Count the number of keypoints on the preceding vehicle for all 10 images and take note of the distribution of their neighborhood size. Do this for all the detectors you have implemented.

Detector	image0	image1	image2	image3	image4	image5	image6	image7	image8	image9	Neighborhood size
SHI-TOMASI	125	118	123	120	120	113	114	123	111	112	4
HARRIS	17	14	18	21	26	43	18	30	26	34	6
FAST	121	115	127	122	111	113	107	103	112	117	7
BRISK	264	282	282	277	297	279	289	272	266	254	21
ORB	92	102	106	113	109	125	130	129	127	128	57
AKAZE	166	157	161	155	163	164	173	175	177	179	7.8
SIFT	138	132	124	137	134	140	137	148	159	137	5.6

MP.7 Performance Evaluation 2

Count the number of matched keypoints for all 10 images using all possible combinations of detectors and descriptors. In the matching step, the BF approach is used with the descriptor distance ratio set to 0.8.

Some combinations of detector and descriptor doesn't make sense, those results are N/A.

Detector,Descriptor	BRISK	BRIEF	ORB	FREAK	AKAZE	SIFT
SHITOMASI	686	922	866	688	N/A	900
HARRIS	138	169	164	134	N/A	167
FAST	638	805	831	645	N/A	763
BRISK	1426	1512	1379	1386	N/A	1529
ORB	678	486	691	395	N/A	742
AKAZE	1020	1169	1096	1002	1199	1176
SIFT	491	695	N/A	492	N/A	759

MP.7 Performance Evaluation 3

Log the time it takes for keypoint detection and descriptor extraction. The results must be entered into a spreadsheet and based on this data, the TOP3 detector / descriptor combinations must be recommended as the best choice for our purpose of detecting keypoints on vehicles.

Some combinations of detector and descriptor doesn't make sense, those results are N/A.

Detector\Descriptor	BRISK	BRIEF	ORB	FREAK	AKAZE	SIFT
SHITOMASI	17.98	21.38	18.8	52.4079	N/A	31.82
HARRIS	13.28	14.50	14.24	32.07	N/A	22.31
FAST	3.98	3.72	3.12	22.91	N/A	14.72
BRISK	34.84	33.42	40.72	54.59	N/A	54.73
ORB	5.82	6.91	11.12	22.52	N/A	23.83
AKAZE	51.55	53.93	57.92	75.42	93.73	67.89
SIFT	68.54	88.18	N/A	113.33	N/A	137.67

The top 3 detector/descriptor combinations are found by evaluating the tables above.

In terms of number of matched keypoints (More is better)

1. BRISK/SIFT
2. BRISK/BRIEF
3. BRISK/BRISK

In terms of execution time (Small is better)

1. FAST/ORB
2. FAST/BRIEF
3. FAST/BRISK