This project is aimed at object tracking, and it involves tracking a predefined object based on its color and predict its trajectory to expect the location it appears again in the event of an occlusion by a different larger object. The project consists of 3 main parts which will make use of basic image processing techniques in openCV with python and MATLAB. Firstly, we try to detect our object of interest which in our case is a ball of a specific color. The detection is based on its color which can be changed as desired before running the code. Second part is building an array for the balls center location (x,y) as a function of time which we feed to the third part; the predictor. Last part is about predicting the trajectory of the ball which is done using mathematical curve fitting techniques. Obviously, the more points we get, the better results we obtain for prediction. The process of detecting and tracking the ball will be simplified by detecting the ball based on its color which will initially involve converting the color space from RGB to HSV. Then an image thresholding is done with boundaries to get a binary image output. We will then need to draw and find contour and extract the biggest bound of the contour on the binary image. This process will be continuous for each frame received from the camera stream. The project output would be interacting with a live video, tracing the ball and drawing a circle around it. Then drawing a line (or curve) for tracing the balls location and finally drawing another line (or curve) with a different color that shows the expected path. The program should track the ball as long as it can see it and when its blocked by an obstacle, its showing the expected path for it and then when it is visible again, it follows the same procedures to track and predict. Ultimately, the project will be able to detect balls based on any color and can be expanded to detect any predefined object. A graphical user interface will also be designed to select the specific color of ball needed to be tracked and will contain several options that will also enhance visualization as needed.
For coding, MATLAB is used.
A function is created to detect the object of interest and it passes the center and radius to the main script which use them to track the path of the ball motion and also to predict the behavior of such motion. For the tracking part, we save the coordinates of the ball center as (x,y) points and plot the last n points where n is predetermined. Also, we create a circle around the ball using the radius found in the ball detection part. To predict the ball motion, we first differentiate the nature of the motion whether it’s a line or a curve and if it’s a line, then we determine if it’s horizontal or vertical one. We make that identification by checking the difference between x values to know if it’s a vertical line; if the difference is close to a specified tolerance value that is relatively close to zero, then it’s considered a vertical line and we plot the predicted path as a vertical line starting from the last position of the ball center to the end of the frame. We adapt the same technique for the horizontal line case. If this is not the case, then we check the ratio of the difference in y values, ∆y, and in x values, ∆x, for the last 3 points which represent 2 slopes. If the 2 slopes are close to each other, then we may consider the path as a linear one and plot a line with a slope equal to the average of the 2 slopes. If the slopes are not close, then we consider it as a curvilinear motion and we use the curve fitting technique to predict the motion. We adapt a second order polynomial function to create our predicted path.
Hassan Shehawy 