How can i prevent my object detection program from detecting multiple objects of different sizes?

Question

So, here is my situation. I have created a object detection program which is based on color object detection. My program detects the color red and it works perfectly. But here is the problems i am facing:-

Whenever there are more than one red object in the surrounding, my program detects them and it cannot really track one object at that time(i.e it tracks other red objects of various sizes in the background. It shows me the error that "too much noise in the background". As you can see in the "threshold image" attached, it detects the round object (which is my tracking object) and my cap which is red in color. I want my program to detect only my tracking object("which is a round shaped coke cap"). How can i achieve that? Please help me out. I have my engineering design contest in few days and i have to demo my program infront of my lecturers. My program should only be able to detect and track the object which i want. Thanks

My code for the objectdetection program is a little long. So, i am hereby explaining the code as follows- I captured a frame from the webcam frame-converted it to HSV- used HSV Inrange filter to filter out the other colors but red- applied morphological operations on the filtered image. This all goes in my main function

I am using a frame resolution of 1280*720 for my webcam frame. It kind of slows down my program but it was a trade off which i had to do for performing gesture controlled operations. Anyways here is my drawobjectfunction and trackfilteredobjectfunction.

int H_MIN = 0;
int H_MAX = 256;
int S_MIN = 0;
int S_MAX = 256;
int V_MIN = 0;
int V_MAX = 256;
//default capture width and height
const int FRAME_WIDTH = 1280;
const int FRAME_HEIGHT = 720;
//max number of objects to be detected in frame
const int MAX_NUM_OBJECTS=50;
//minimum and maximum object area
const int MIN_OBJECT_AREA = 20*20;
const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;


void drawObject(int x, int y,Mat &frame){

circle(frame,Point(x,y),20,Scalar(0,255,0),2);
    if(y-25>0)
    line(frame,Point(x,y),Point(x,y-25),Scalar(0,255,0),2);
    else line(frame,Point(x,y),Point(x,0),Scalar(0,255,0),2);
    if(y+25<FRAME_HEIGHT)
    line(frame,Point(x,y),Point(x,y+25),Scalar(0,255,0),2);
    else line(frame,Point(x,y),Point(x,FRAME_HEIGHT),Scalar(0,255,0),2);
    if(x-25>0)
    line(frame,Point(x,y),Point(x-25,y),Scalar(0,255,0),2);
    else line(frame,Point(x,y),Point(0,y),Scalar(0,255,0),2);
    if(x+25<FRAME_WIDTH)
    line(frame,Point(x,y),Point(x+25,y),Scalar(0,255,0),2);
    else line(frame,Point(x,y),Point(FRAME_WIDTH,y),Scalar(0,255,0),2);

    putText(frame,intToString(x)+","+intToString(y),Point(x,y+30),1,1,Scalar(0,255,0),2);

}



void trackFilteredObject(int &x, int &y, Mat threshold, Mat &cameraFeed){

    Mat temp;
    threshold.copyTo(temp);
    //these two vectors needed for output of findContours
    vector< vector<Point> > contours;
    vector<Vec4i> hierarchy;
    //find contours of filtered image using openCV findContours function
    findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );
    //use moments method to find our filtered object
    double refArea = 0;
    bool objectFound = false;
    if (hierarchy.size() > 0) {
        int numObjects = hierarchy.size();
        //if number of objects greater than MAX_NUM_OBJECTS we have a noisy filter
        if(numObjects<MAX_NUM_OBJECTS){
            for (int index = 0; index >= 0; index = hierarchy[index][0]) {

                Moments moment = moments((cv::Mat)contours[index]);
                double area = moment.m00;

                //if the area is less than 20 px by 20px then it is probably just noise
                //if the area is the same as the 3/2 of the image size, probably just a bad filter
                //we only want the object with the largest area so we safe a reference area each
                //iteration and compare it to the area in the next iteration.
                if(area>MIN_OBJECT_AREA && area<MAX_OBJECT_AREA && area>refArea){
                    x = moment.m10/area;
                    y = moment.m01/area;
                    objectFound = true;
                    refArea = area;
                }else objectFound = false;


            }
            //let user know you found an object
            if(objectFound ==true){
                putText(cameraFeed,"Tracking Object",Point(0,50),2,1,Scalar(0,255,0),2);
                //draw object location on screen
                drawObject(x,y,cameraFeed);}

        }else putText(cameraFeed,"TOO MUCH NOISE! ADJUST FILTER",Point(0,50),1,2,Scalar(0,0,255),2);
    }
}

Here is the link of the image; as you can see it also detects the red hat in the background along with the red cap of the coke bottle.

My observations:- Here is what i think, to achieve my desired goal of not detecting objects of unknown sizes of red color. I think i have to edit the value of maximum object area which i declared in the above program as (const int MAX_OBJECT_AREA = FRAME_HEIGHT*FRAME_WIDTH/1.5;). I think i have to change this value, that might eliminate the detection of bigger continous red pictures. But also, there is another problem some objects are not completely red in color and they have patches of red and other colors. So, if the detected area is within the range specfied in my program then my program detects those red patches too. What i mean to say is i was wearing a tshirt which has mixed colors and when i tested my program by wearing that tshirt, my program was able to detect the red color out of the other colors. Now, how do i solve this issue?

Answer 1

I think you can try out the following procedure:

• obtain a circular kernel having roughly the same area as your object of interest. You can do it like: Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(d, d)); where d is the diameter of the disk.
• perform normalized-cross-correlation or convolution of the filtered regions image with this kernel (I think normalized-cross-correlation would be better. And add an empty boarder around the kernel).
• the peak of the resulting image should give you the location of the circular region in your filtered image (if you are using normalized-cross-correlation, you'll have to add the shift).

To speed things up, you can perform this at a reduced resolution.

Answer 2

You can filter out non-circular shapes by detecting circles in your thresholded image. OpenCV provides a built-on method to detect circles using Hough transform, more info here. You can take advantage of this function to retain only circles that have a radius in a given range.

Another possibility is to implement connected component labeling (CCL) into your demo program. I believe that it was removed at some point in verions 2.x of OpenCV, but a basic implementation of the two-pass version is straightforward from the Wikipedia page. CCL will assign a unique ID for each object after thresholding. You then have to implement matching between the objects at frame (T-1) and objects in frame (T) (for example based on some nearest distance criterion) and possibly trajectory filtering or smoothing, but this would definitely give you some extra-points.