1. Yusuf Ziya Uzun
Artificial Neural Networks – CMP5133

2.  Resample video to desired size
 Divide video to Images
 Capture OF (Optical Flow) Vectors
 DBSCAN clustering
 Find OF Vector orientation
 Colorize clusters by using vector orientations

3.  The ground truth does not exist: The desired
results always depend on the user
requirements and specifications.
 Even for a fixed image, there may be more
than one "best" segmentation because the
criteria defining the quality of a
segmentation are application dependent.
-Pierre Soille

4.  Motion: displacement, direction, velocity,
acceleration, time and speed
 Optical Flow: distribution of the apparent
velocities of objects in an image
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5.  Two Main Category: Sparse and Dense
 Horn and Schunck
 Kanade-Lucas-Tomasi(KLT)
 Gunnar - Farneback

6.  Separate moving objects from background by
using motion vectors(optical flow)
 Just split image N pieces.
 Problems:
 Aperture
 Barber-pole (Motion vs Optical)
 Closer Objects Have Bigger Velocity?
 Stereo Vision

7.  Density-based spatial clustering of
applications with noise (DBSCAN)
 Given a set of points and radius:
 Groups close points
 Alone points become outliers

8.  C# and EmguCV
 Resampling video with ffmpeg manually
 Ratio: same in video
 Size: 640 px width
 Divide video and capture frames (x – 5) and x
to compare
 OF Vectors:
 Gunnar – Farneback Dense OF Vectors
 Gaussian Box Filter
 A global threshold to remove noise

9.  DBSCAN:
 Globally defined epsilon and # of points
 Computing clusters of OF vectors
 OF vector orientation
 Coloring clusters by looking OF vector
orientations

11.  Many Global Variables
 DBSCAN and OF combination useful
 Experimental
 Variables Domain Dependent
 Not good to use everywhere
 Can combine with Supervised Learning