Title
Coherent Motion Segmentation in Moving Camera Videos Using Optical Flow Orientations
Abstract
In moving camera videos, motion segmentation is commonly performed using the image plane motion of pixels, or optical flow. However, objects that are at different depths from the camera can exhibit different optical flows even if they share the same real-world motion. This can cause a depth-dependent segmentation of the scene. Our goal is to develop a segmentation algorithm that clusters pixels that have similar real-world motion irrespective of their depth in the scene. Our solution uses optical flow orientations instead of the complete vectors and exploits the well-known property that under camera translation, optical flow orientations are independent of object depth. We introduce a probabilistic model that automatically estimates the number of observed independent motions and results in a labeling that is consistent with real-world motion in the scene. The result of our system is that static objects are correctly identified as one segment, even if they are at different depths. Color features and information from previous frames in the video sequence are used to correct occasional errors due to the orientation-based segmentation. We present results on more than thirty videos from different benchmarks. The system is particularly robust on complex background scenes containing objects at significantly different depths.
Year
DOI
Venue
2013
10.1109/ICCV.2013.199
Computer Vision
Keywords
Field
DocType
depth-dependent segmentation,optical flow orientations,image plane motion,similar real-world motion,different optical flow,different depth,observed independent motion,real-world motion,coherent motion segmentation,optical flow,different benchmarks,motion segmentation,image segmentation
Computer vision,Scale-space segmentation,Quarter-pixel motion,Motion field,Pattern recognition,Computer science,Range segmentation,Image segmentation,Artificial intelligence,Motion estimation,Optical flow,Match moving
Conference
Volume
Issue
ISSN
abs/1511.01619
1
1550-5499
Citations 
PageRank 
References 
31
0.82
20
Authors
3
Name
Order
Citations
PageRank
Manjunath Narayana1793.48
Allen Hanson221133.75
Erik G. Miller31861126.56