caixh39/Object-Tracking

this object tracking library is optimized using eigen3 and openMP

Object-Tracking

This object tracking library is optimized by using Eigen3 and openMP

•   Author: Haibo Wang
  
•   Email: dasuda2015@163.com
•   This is an open source Object-Tracking library, The development language is C++, The classic and mainstream object tracking algorithms and related algorithms are reproduced(For example, RANSAC, least squares and so on). Of course, these are optimized by Eigen3 and openMP.

Hardware platform:

•   CPU: intel i7 6700K
  
•   Memory:8G x 2 DDR4

Performance:

• RANSAC: Linear Fitting, 600 sample point, 500 iteration, Time consuming:*100us *
  
• Kalman Filter: Two dimensional coordinate tracking, System state variable is [x,y,dx,dy],prediction+update,Mean time consuming:8us
  
• MeanShift:using kernel function,refactoring with Eigen3 and openMP.

1.Camera calibration(If you want to do something good, you mast sharpen it firstly)

• Use opencv's own calibration tool to calculate the internal parameters of the camera, the chessboard picture has been given, fool-style operation, take a look.

Location:

	DasudaRunner/Object-Tracking/calibration_camera

Requirments:

	opencv: >=3.3.0 or >=3.0.0
	python: >=2.7
	python-opencv:

Quick Start:

1.1 Connecte Camera, get images containing checkerboard.

	python getChecker.py # press 's' to save image

1.2 get images list file

	./create_imagelist imagelist.yaml *.jpg

1.3 Calculate the internal parameters of the camera.

	./calibration -w=7 -h=7 imagelist.yaml

1.4 Of course, I encapsulated these commands<1.2,1.3> into a script file, and you can also run the following command.

	sudo sh calibration_camera.sh

  get out_camera_data.yml , this is a file that contains your camera's internal reference.

1.5 You can run ./demo, and get corrected images.

	./demo