The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Video Segmentation by Tracking Many Figure-Ground Segments
Open accessAbstract
We propose an unsupervised video segmentation approach by simultaneously tracking multiple holistic figure-ground segments. Segment tracks are initialized from a pool of segment proposals generated from a figure-ground segmentation algorithm. Then, online non-local appearance models are trained incrementally for each track using a multi-output regularized least squares formulation. By using the same set of training examples for all segment tracks, a computational trick allows us to track hundreds of segment tracks efficiently, as well as perform optimal online updates in closed-form. Besides, a new composite statistical inference approach is proposed for refining the obtained segment tracks, which breaks down the initial segment proposals and recombines for better ones by utilizing high-order statistic estimates from the appearance model and enforcing temporal consistency. For evaluating the algorithm, a dataset, SegTrack v2, is collected with about 1,000 frames with pixel-level annotations. The proposed framework outperforms state-of-the-art approaches in the dataset, showing its efficiency and robustness to challenges in different video sequences.
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Robust object tracking with online multiple instance learning
Babenko, B., Yang, M.-H., & Belongie, S. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.226 · Cited by 2,007
Evaluating Color Descriptors for Object and Scene Recognition
van de Sande, K. E. A., Gevers, T., & Snoek, C. G. M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.154 · Cited by 1,783
Secrets of optical flow estimation and their principles
Sun, D., Roth, S., & Black, M. J. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539939 · Cited by 1,389
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295
People-tracking-by-detection and people-detection-by-tracking
Andriluka, M., Roth, S., & Schiele, B. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587583 · Cited by 824
Globally-optimal greedy algorithms for tracking a variable number of objects
Pirsiavash, H., Ramanan, D., & Fowlkes, C. C. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995604 · Cited by 790
Object Segmentation by Long Term Analysis of Point Trajectories
Brox, T., & Malik, J. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15555-0_21 · Cited by 744
Efficient Additive Kernels via Explicit Feature Maps
Vedaldi, A., & Zisserman, A. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.153 · Cited by 688
Discovering important people and objects for egocentric video summarization
Lee, Y. J., Ghosh, J., & Grauman, K. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247820 · Cited by 658
Efficient hierarchical graph-based video segmentation
Grundmann, M., Kwatra, V., Han, M., & Essa, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539893 · Cited by 628
Cited by
Multiple Hypothesis Tracking Revisited
Kim, C., Li, F., Ciptadi, A., & Rehg, J. M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.533 · Cited by 666
Self-citationMOTS: Multi-Object Tracking and Segmentation
Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B. B. G., Geiger, A., & Leibe, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00813 · Cited by 658
Survey on semantic segmentation using deep learning techniques
Lateef, F., & Ruichek, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.02.003 · Cited by 559
Saliency-Aware Video Object Segmentation
Wang, W., Shen, J., Yang, R., & Porikli, F. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2662005 · Cited by 472
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Oh, S. W., Lee, J.-Y., Sunkavalli, K., & Kim, S. J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00770 · Cited by 420
Metadata
- Authors
- Li, F., Kim, T., Humayun, A., Tsai, D., & Rehg, J. M.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 2192-2199
- OpenAlex
- W2138682569
- Cited by
- 512
- References
- 33
- FWCI
- 17.07
- Type
- conference paper
- Language
- en
- Open access
- green
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- Li, F., Kim, T., Humayun, A., Tsai, D., & Rehg, J. M. (2013). Video Segmentation by Tracking Many Figure-Ground Segments. 2013 IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.2013.273
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