Paper Info
Title
Object Detection, Tracking, and Motion Segmentation for Object-level Video Segmentation.
Abstract
We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.
Year
Venue
Field
2016
arXiv: Computer Vision and Pattern Recognition
Computer vision,Motion cues,Object detection,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Image segmentation,Video tracking,Artificial intelligence,Detector
DocType
Volume
Citations 
Journal
abs/1608.03066
2
PageRank 
References 
Authors
0.37
20
2
Name
Order
Citations
PageRank
Benjamin Drayer1121.56
Thomas Brox27866327.52