Detecting objects in our disparity map

Now that we are fairly happy with our disparity map, we need a way to find objects in it, so that we can calculate the distance to them and decide whether the car needs to stop or not.

There are several methods that we came across for doing this, but the one we’ve decided on is segmenting the image via blob detection.

I started implementing this from scratch using contours, where you detect the contours in an image, and that essentially gives you a closed region of the image. You can then do further analysis on the contours, and discard those which are below a certain area, calculate the mean intensity of the pixels within a contour and so on.

Figure 1 Result of extracting contours, and filling them in with a random colour.

That’s how far I had got with my implementation when I discovered the cvBlobsLib library. It’s a complete library for blob detecting that integrates with OpenCV (note that OpenCV has a SimpleBlobDetector class but that is quite limited at the moment). cvBlobsLib basically implements all of the features that we might require, and probably does them faster than I could implemented, so why reinvent the wheel, right?

Installing cvBlobsLib on Linux

First, you must download appropriate archive for Linux from here. Extract the contents in a directory on your desktop, then follow the instructions in the readme file.

Then, in your eclipse project, under C/C++ Build -> Settings -> GCC C++ Linker -> Libraries, add blob in Libraries(-l), and under GCC C++ Compiler -> Includes, add /usr/local/include/cvblobs. And finally, in the working .cpp file, add an include directive #include <cvblobs/blob.h> (or #include “blob.h” if you stored the header files locally).

Using cvBlobsLib

cvBlobsLib only works with the C style IplImage object instead of the Mat object in OpenCV, but converting between the two is not is not that big an issue. Plus you can only change the header file, and then not have to copy all of the data from one format to the other, so there is no real performance impact.

//bm is our disparity map in a Mat
IplImage *dispIpl = new IplImage(bm);	//create an IplImage from Mat

//Declare variables
CBlobResult blobs;
CBlob *currentBlob;
int minArea = 1;

blobs = CBlobResult(dispIpl, NULL,0);  //get all blobs in the disparity map
blobs.Filter( blobs, B_EXCLUDE, CBlobGetArea(), B_LESS, minArea ); //filter blobs by area and remove all less than minArea

//Display blobs
IplImage *displayedImage = cvCreateImage(Size(640,480),8,3); //create image for outputting blobs
for (int i = 0; i &lt; blobs.GetNumBlobs(); i++ )
	currentBlob = blobs.GetBlob(i);
	Scalar color( rand()&amp;255, rand()&amp;255, rand()&amp;255 );
	currentBlob-&gt;FillBlob( displayedImage, color);
Mat displayImage = displayedImage; //Convert to Mat for use in imshow()
imshow("Blobs", displayImage);

Figure 2 Disparity map

Figure 3 Result of blob detection with minArea set to 1

We can also do some noise filtering by excluding blobs that are below a certain size.

Figure 4 Result of blob detection with minArea set to 50. Note that there is a lot less noise.

Another big problem we can see immediately is that the person in the foreground and the car in the background are detected as one region. This is because the edge of the person is not closed, and so if you were to draw a contour, it would go around the edges of the person and the car, like so:

Figure 5 Contours in disparity map. Note that the person and the object to the right share are surrounded by the same contour.

We’ve dealt with this problem using morphological filters.

Morphological filtering

“Morphological filtering is a theory developed in the 1960s for the analysis and processing of discrete images. It defines a series of operators which transform an image by probing it with a predefined shape element. The way this shape element intersects the neighbourhood of a pixel determines the result of the operation” [1].

OpenCV implements Erosion and dilation filters as simple functions. For this problem we need to use the erode filter.

erode(src, dst, Mat());  //default kernel is of size 3 x3

//Optionally, select kernel size
cv::Mat element(7,7,CV_8U,cv::Scalar(1));
erode(src, dst, element);

Figure 6 Filter with a 2×2 kernel. It does the job! But we need to experiment with more images to set a final value for the kernel size.

Figure 7 Erosion with kernel size 3×3 (left) and 7×7 (right) for illustration purposes. 7×7 is clearly very destructive.


We’ve isolated our objects of interest! Now all that remains to be done is go over the blobs, find their average intensity, and calculate the distance!

Sources: [1], cvBlobsLib



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