Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation

Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods leverage motion cues obtained from optical flow maps in addition to appearance cues to exploit the property that salient obje...

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Bibliographic Details
Main Authors: Cho, Suhwan, Lee, Minhyeok, Lee, Seunghoon, Park, Chaewon, Kim, Donghyeong, Lee, Sangyoun
Format: Conference Proceeding
Language:English
Subjects:
action recognition
Algorithms: Image recognition and understanding (object detection
Benchmark testing
categorization
Embedded sensing/real-time techniques
etc.
Federated learning
Object segmentation
Real-time systems
segmentation
Solids
Streaming media
Video recognition and understanding (tracking
Video sequences
Online Access:Request full text
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