MareArts Computer Vision Study.: Projects

Showing posts with label Projects. Show all posts

6/06/2021

Real-time stitching multi-video to one screen

* Introduction

- The solution shows panorama image from multi images. The panorama images is processing by real-time stitching algorithm.

- Each cameras has a limited field of view, but the solution can be monitoring large areas from merged into a panorama image.

- The performance is excellent with the following technical configuration.
. real time image processing using GPU.
. Accurate calculation of R, T, K (Rotation, Translation, Camera intrinsic) between each camera with nonlinear optimization
. Color calibration using the exposure blending

- The solution can be applied efficiently and easy in Military Region, tourist attractions, intersections, ports

* Real-time N to 1 stitching algorithm

- Existing stitching algorithm is modified to separate 2 parts of offline and online processing for more efficient realtime processing.

- The Off-Line processing part is calculated first time or if the matching inaccurate.

- On-Line processing part is a routine to create the panoramic image by warping (Warping) calculated by the matching, the blending value.

No ordered input images

- Feature extraction and to calculate the homography matrix between each image by evaluating (RANSAC), and set image position through matching rate.
- To get correct R, T using bundle adjustment

- Searching the overlap region, the blending coefficient is determined with respect to the non-overlapping region.

- Obtained R, T, K, and connected by warping the images and blending and complete the panorama finally

* Experiment

- 4 real-time video stitching speed of about 10~20 fps (Intel® core™ i5-3570 cpu 3.40GHz, NVIDIA Geforce GTX 650)

See the result on youtube

- https://www.youtube.com/watch?v=e6ociYYQS4U

- https://www.youtube.com/watch?v=jP1Y8nd0mQc

- https://www.youtube.com/watch?v=Zkrn-rooj0M

- https://www.youtube.com/watch?v=g9pL7cyL8H0

- https://www.youtube.com/watch?v=xqnHBaZU1R4

about source code and Stitching class here :
http://study.marearts.com/2016/10/real-time-n-camera-stitching-class.html

updating 42/06/2018
I have decided to sell source code ^^
If you have interest, go to here, you can buy source code

** 2021.06 updated **

realtime stitching SDK:

https://www.marearts.com/Realtime-Stitching-SDK-ba67e56550a14688835f291f891af4ac

1/26/2015

Real-time yard trailer identification by detection of vehicle ID numbers

Real-time yard trailer identification by detection of vehicle ID numbers

Project period : (2013.09~2013.11)

*Introduction

• Y/T(Yard Trailer) Y/T(Yard Trailer) number identification solution using image processing.
• The solution using camera is easy to installation and maintance compare to the RFID. And It is more free from distance constraint.
• Machine learning methods - SVM (Support Vector Machine), MLP (Multi Layers Perceptron) are used to recognize the number ID
• A high-speed image processing through the GPU parallel programming

*Real-time pre-processing for features extraction

• The process of preprocessing for ID number extraction
-In the first step, we apply different filters, morphological operations, contour algorithms, and validations to retrieve those parts of the image that could have targeted region.
-Especially, we targeted to detect a ventilating opening instead of number ID, because that target is less shape change than the 3 characters of number ID.

*Vehicle Identification

• HOG(Histogram of gradient) feature extraction and SVM machine learning to detect a ventilating opening

• Each segmented character is to extract the features for training and classifying the MLP algorithm
• The feature is horizontal, vertical histogram values from 5x5 low resolution image.

*Experiment

• Recognition rate over the 95%
• Detection speed about 0.05 sec/frame (Image size : 1280x720, Intel® core™ i5-3570 cpu 3.40GHz, NVIDIA Geforce GTX 650)
• The trailer enter speed about 20~30 km/h

#include < stdio.h>

#include "ShinPortOCR.h"


void main()
{

 ShinPortOCR cShinPortOCR;

 //printf("연속으로 읽을 이미지 파일 갯수? (ex:10 -> ./data/1.jpg, ./data/2.jpg ... ./data/10.jpg) \n");
 printf("How many images do you want to test? (ex:100, 500,  1630\n");
 int num;
 scanf_s("%d", &num);

 int p = 0, n = 0;
 char str[100];
 for (int i = 0; i< num; ++i)
 {
  printf("%d/%d\n", i, num);

  sprintf_s(str, "./data/%d.jpg", i + 1);
  Mat inImg = imread(str, 1);//, CV_LOAD_IMAGE_GRAYSCALE);
  Mat OutImg; 
  if (cShinPortOCR.GoGoXing(inImg, OutImg, 1) == -111) //1 is debug print, 0 is no dubug out
  {
   sprintf_s(str, ".\\Log\\fail\\%d.jpg", i + 1);
   imwrite(str, inImg);
  }
  else{
   sprintf_s(str, ".\\Log\\success\\%d.jpg", i + 1);
   imwrite(str, inImg);
  }


  sprintf_s(str, ".\\Log\\processing\\%d.jpg", i + 1);
  imwrite(str, OutImg);


  imshow("result", OutImg);
  waitKey(10);

 }
}

///

Source code is here
https://github.com/MareArts/Container-Yard-Trailer-ID-number-recognition

you can down opencv dll/lib/header files on here
opencv 249 64bit cuda 60
https://www.amazon.com/clouddrive/share/7bPR5HgbCbNZJHwG0ldq1gwHtydLXRxtQVYc5JYPlSF?ref_=cd_ph_share_link_copy

11/11/2011

Project - Embeded camera R&D for Real-Time fire detection surveillance in tunnel environment

Embeded camera R&D for Real-Time fire detection surveillance in tunnel environment

- 2008.07.01~2009.06.30
- High Computing Power Embeded Camera R&D, Fire & Smoke Detection(My Job)

The goal of the project is to develop embeded camera for detect fire and smoke.
My job was an algorithm R&D for detection fire & smoke and the s/w programing.
The fire detection algorithm uses the HMM algorithm.
The features of the fire's sequence is learned in the off-line. And then the learned Markov model is used in the oline using viterbi algorithm.

Below Movies are the result of the project.

*** Demo 1, Fire detection Simple situation :

*** Demo 2, Fire detection in the road, There is similar light of the car with fire color:

***Demo 3, Fire detection test in the real fire situation :

***Demo 4, Embeded Camera Test. The camera detects the fire and then zoom in the region of the fire.

***Demo 5, Smoke detection test.

10/06/2011

10th unmanned vehicle contest. The sponsor is Hyundai-Kia motors(Korea)

In 2010, I entered 10th unmanned vehicle contest.
The competition was sponsored by by Hyundai-Kia Motors.
In the our team, my part was software.
I had to solve many problems.
For example; Obstacle Detection and avoidance, mapping, path planing, Localization...
During prepare the competition, I programed nearly 30,000 lines. ^^
My team didn't acomplish the win. But our vehicle have finished all course as unmanned.
And I did my best. so I do not regret it.

OBS have broadcasted this competition.

This movie captured while unmaned vehicle driving.

This is architecture of software.

We have used many sensors and equipments.

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