Moving-Object-Detection-and-Trajectory-Estimation

ENPM673 – Perception for Autonomous Robots

Project 1

Problem 1: In the given video, a red ball is thrown against a wall. Assuming that the trajectory of the ball follows the equation of a parabola:

• 1. Detect and plot the pixel coordinates of the center point of the ball in the video. [10] (Hint: Read the video using OpenCV’s inbuilt function. For each frame, filter the red channel)
• 2. Use Standard Least Squares to fit a curve to the extracted coordinates. For the estimated parabola you must, a. Print the equation of the curve. [5] b. Plot the data with your best fit curve. [5] Assuming that the origin of the video is at the top-left of the frame as shown below, compute the x-coordinate of the ball’s landing spot in pixels, if the y-coordinate of the landing spot is defined as 300 pixels greater than its first detected location. [10] https://drive.google.com/file/d/1kP5n9sWoRFbt2p3_lJAqz86Zcp1QC5jF/view?usp=drive_link https://youtu.be/VObA5VUwynI

A. File Structure

This projects consists of the following code files

• Problem #1:
  1. redball_trajectory.py
• Problem #2:
  1. covariance_surf_normal.py
  2. lstq.py
  3. tsl.py
  4. ransac.py

B. Modification to the given dataset:

• Add the header to the 3 given columns as 'x','y','z' respectively

C. Dependancies

• Ensure the following depenancies are installed

  pip install pandas
  pip install numpy
  pip install scipy
  pip install matplotlib
  pip install opencv-python
  

• Ensure that the above programs are downloaded into the same folder containing 'pc1.csv' and 'pc2.csv' files

D. Running the Program

• Run the programs individually to check the outputs.

• For lstq.py, tsl.py, ransac.py, you may need to change the file name for two different datasets.

  python3 <file_name>
  

E. Results

• On running each of the proframs, the output either pops out a plot or a video in individual window. The outputs can be correlated with the outputs shown in the report.