The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions
Open accessAbstract
In this paper, we propose a novel approach to extract primary object segments in videos in the `object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.
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Cites
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Self-citation]Video object segmentation by hypergraph cut
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]Video object segmentation by hypergraph cut
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Cited by
See More, Know More: Unsupervised Video Object Segmentation With Co-Attention Siamese Networks
Lu, X., Wang, W., Ma, C., Shen, J., Shao, L., & Porikli, F. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00374 · Cited by 521
Review of Visual Saliency Detection With Comprehensive Information
Cong, R., Lei, J., Fu, H., Cheng, M.-M., Lin, W., & Huang, Q. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870832 · Cited by 419
Jain, S. D., Xiong, B., & Grauman, K. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.228 · Cited by 361
Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks
Wang, W., Lu, X., Shen, J., Crandall, D., & Shao, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00933 · Cited by 303
Learning Motion Patterns in Videos
Tokmakov, P., Alahari, K., & Schmid, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.64 · Cited by 267
Metadata
- Authors
- Zhang, D., Javed, O., & Shah, M.
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 628-635
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2155598147
- Cited by
- 309
- References
- 20
- FWCI
- 27.09
- Type
- conference paper
- Language
- en
- Open access
- green
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