(a) What is meant by the term "tracking by detection" in computer vision?
(b) Briefly describe the difference between such a tracker and the alpha-beta tracker.
Exercise 2: Kalman Filter
Assuming a state vector containing position and speed, and measurements of only position, write down a state evolution function / matrix and measurement model matrix for a constant velocity model.
Exercise 3: Kalman Filter
Write down how you would change the state vector , state evolution matrix, and measurement model matrix, from Exercise 2, for a constant position model (where you believe that the value is not changing but is only corrupted by noise).
Exercise 4: Multi-Object Tracking: Data Association
Briefly describe an advantage of using GNNSF over MHT.
The goal of this part of the programming assignment is for you to learn more about the practical issues that arise when designing a tracking system. You are asked to track moving objects in video sequences, i.e., identifying the same object from frame to frame:
We provided two datasets for you.
1) The bat dataset shows bats in flight, where
the bats appear bright against a dark sky. We included both grayscale and false-color images from this thermal image sequence; you may use whichever images you
2) The cell dataset shows mouse muscle stem cells moving in hydrogel microwells,
the brightness of the pixels within the cells are very similar to the values of the background.
Just in case you had any trouble segmenting images and distinguishing objects (multi-object labeling), we are providing segmentation and/or detections for the bat dataset. The segmentation of the bat dataset is provided in a set of label maps. There is one number per pixel, delimited by commas. Pixels with the value 0 are background. The maps are 1024 by 1024. The detections are given in a comma delimited file, one for each frame. There is one point per line. Each point is given as the X coordinate followed by the Y coordinate, delimited by commas.
For the cell dataset, no segmentation is provided. It's your task to do both segmentation and tracking. Note: 1) The filopodia, which cells have during migration, are long "feet" that are difficult to outline automatically. 2) Some cells spit into daughter cells. Since accurate cell segmentation is very challenging, to obtain full credit, your focus should be on the multi-object tracking task (and detecting the birth of a new cell), while your segmentation result can be relatively coarse.
You do not need to use our segmentation/detection if you would like to see the results using your own algorithms. You may use any of your code from the previous assignments and any library functions you wish.
Display the results of your tracking algorithm on top of the original images. Use different colors to show that you successfully maintain track identity. Draw lines to show the history of the flight trajectories.
In your write-up, you should discuss the following items:
You will demonstrate your code to one of the graders according to the demo schedule. Be prepared to show your code working and discuss any important issues that you discovered and how you addressed them.
|Margrit Betke, Professor Computer Science Department Email: email@example.com|