Motion Tracking
#1

Introduction
Video contains motion information which can be used for
detecting the presence of moving objects
tracking and analyzing the motion of the objects
tracking and analyzing the motion of camera
Basic tracking methods:
Gradient-based Image Flow:

Track points based on intensity gradient.
Example: Lucas-Kanade method [LK81, TK91].
Feature-based Image Flow:
Track points based on template matching of features at points.
Mean Shift Tracking:
Track image patches based on feature distributions, e.g., color
histograms [CRM00].
Strengths and Weaknesses
Image flow approach:
Very general and easy to use.
If track correctly, can obtain precise trajectory with sub-pixel
accuracy.
Easily confused by points with similar features.
Cannot handle occlusion.
Cannot differentiate between planner motion and motion in depth.
Demo: lk-elephant.mpg.
Mean shift tracking:
Very general and easy to use.
Can track objects that change size & orientation.
Can handle occlusion, size change.
Track trajectory not as precise.
Can’t track object boundaries accurately.
Demo: ms-football1.avi, ms-football2.avi.
Notes:
The chances of making wrong association is reduced if we can
correctly predict where the objects will be in frame 2.
To predict ahead of time, need to estimate the velocities and the
positions of the objects in frame 1.
To overcome these problems, need more sophisticated tracking
algorithms:
Kalman filtering: for linear dynamic systems, unimodal
probability distributions
Extended Kalman filtering: for nonlinear dynamic systems,
unimodal probability distributions
Condensation algorithm: for multi-modal probability distributions


DOWNLOAD FULL REPORT
http://comp.nus.edu.sg/~cs6240/lecture/tracking.pdf
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