an application of video segmentation using optical flows
TRANSCRIPT
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AN APPLICATION OF VIDEO
SEGMENTATION USING OPTICAL
FLOWS AND DBSCAN CLUSTERING
Yusuf Ziya Uzun
Artificial Neural Networks – CMP5133
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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
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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
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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
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OPTICAL FLOW METHODS Two Main Category: Sparse and Dense
Horn and Schunck Kanade-Lucas-Tomasi(KLT)Gunnar - Farneback
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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
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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
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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
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IMPLEMENTATION DBSCAN:
Globally defined epsilon and # of pointsComputing clusters of OF vectors
OF vector orientationColoring clusters by looking OF vector
orientations
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DEMO
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CONCLUSION Many Global Variables DBSCAN and OF combination useful Experimental Variables Domain Dependent Not good to use everywhere Can combine with Supervised Learning
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THANK YOU