这是一篇来自美国的python代写作业案例分享
Instructions
- Integrity and collaboration: Students are encouraged to work in groups but each student must submit their own work. If you work as a group, include the names of your collaborators in your write-up. Code should NOT be shared or copied. Please DO NOT use external code unless permitted. Plagiarism is strongly prohibited and may lead to failure of this course.
- Start early! Running the code on the videos can take a lot of time, making debugging very slow.
- Verify your implmenentation as you proceed! Otherwise you risk having a huge mess of malfunctioning code that can go wrong anywhere.
- Questions: If you have any question, please look at Discussion on canvas fifirst. Other students may have encountered the same problem, and might have been solved already. If not, post your question on the discussion board. TA will respond as soon as possible.
- Write-up: Your write-up should mainly consist of two parts, your answers to theory questions and your insights as you attempt the programming questions. Specifific items to be included in the write-up are mentioned in each question.
- Code: Stick to the function prototypes mentioned in the handout. This makes verifying code easier for the TA. If you do want to change a function prototype or add an extra parameter, please talk to the TA.
- Submission: Your submission for this assignment should be a zip fifile,<First_FmilyName.zip>. composed of your write-up, your Matlab implementations (including helper functions). Please make sure to remove the data/ folder and any other fifiles that are not required. Ensure that your submission is of a reasonable size. You may want to use video compression if your videos are huge.
Your fifinal upload should have the fifiles arranged in this layout:
- <First_FmilyName>.zip
– <First_FmilyName>/
∗ <First_FmilyName>.pdf ∗
matlab/
- LucasKanade.m.m
- affineMBTracker.m
- initAffineMBTracker.m
- lk demo.m
- mb demo.m
∗ results/
- car.mp4
Overview
One incredibly important aspect of human and animal vision is the ability to follow objects and people in our view. Whether it is a tiger chasing its prey, or you trying to catch a basketball, tracking is so integral to our everyday lives that we forget how much we rely on In this assignment, you will be implementing an algorithm that will track an object in a video.
You will fifirst implement the Lucas-Kanade tracker, and then a more computationally effiffifficient version called the Matthew-Baker (or inverse compositional) method [1]. This method is one of the most commonly used methods in computer vision due to its simplicity and wide applicability. We have provided two video sequences: a car on a road, and a helicopter approaching a runway.
To initialize the tracker you need to defifine a template by drawing a bounding box around the object to be tracked in the fifirst frame of the video. For each of the subsequent frames the tracker will update an affiffiffine transform that warps the current frame so that the template in the fifirst frame is aligned with the warped current frame.
