An Application of Video Segmentation Using Optical Flows
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Transcript of An Application of Video Segmentation Using Optical Flows
AN APPLICATION OF VIDEO
SEGMENTATION USING OPTICAL
FLOWS AND DBSCAN CLUSTERING
Yusuf Ziya Uzun
Artificial Neural Networks – CMP5133
OBJECTIVES 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
A PROBLEM ABOUT VIDEO SEGMENTATION 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
OPTICAL FLOW Motion: displacement, direction,
velocity, acceleration, time and speed Optical Flow: distribution of the
apparent velocities of objects in an image
Zoom out Zoom in Pan right to left
OPTICAL FLOW METHODS Two Main Category: Sparse and Dense
Horn and Schunck Kanade-Lucas-Tomasi(KLT)Gunnar - Farneback
OPTICAL FLOW SEGMENTATION Separate moving objects from
background by using motion vectors(optical flow) Just split image N pieces.
Problems:ApertureBarber-pole (Motion vs Optical)
Closer Objects Have Bigger Velocity? Stereo Vision
CLUSTERING: DBSCAN Density-based spatial clustering of
applications with noise (DBSCAN) Given a set of points and radius:
Groups close points Alone points become outliers
IMPLEMENTATION C# and EmguCV Resampling video with ffmpeg manually
Ratio: same in videoSize: 640 px width
Divide video and capture frames (x – 5) and x to compareOF Vectors:
Gunnar – Farneback Dense OF Vectors Gaussian Box Filter A global threshold to remove noise
IMPLEMENTATION DBSCAN:
Globally defined epsilon and # of pointsComputing clusters of OF vectors
OF vector orientationColoring clusters by looking OF vector
orientations
DEMO
CONCLUSION Many Global Variables DBSCAN and OF combination useful Experimental Variables Domain Dependent Not good to use everywhere Can combine with Supervised Learning
THANK YOU