Vision Seminar
Navigation
| News | |
| Main Course Material | |
| Course Outline | |
| | |
| Full Teaching List |
CSE 705: Vision Seminar on Spatiotemporal Video Analysis
SUNY at Buffalo
Fall 2010
| Instructors: | Jason Corso (jcorso), Raymond Fu (yunfu) |
| Course Webpage: | http://www.cse.buffalo.edu/~jcorso/t/2010F_SEM |
| Meeting Times: | M 11-1 |
| Location: | Vision Lab, Lockwood B20A |
| Office Hours: | (Corso) M 4-5, F 2-3 |
News
- First meeting is Monday, 8/30, to discuss paper list and logistics.
Main Course Material
Course Overview: This is a seminar course covering spatiotemporal video analysis. We will read and discuss papers on this topic throughout the semester, with the students primarily in charge of leading the discussions.
Prerequisites: It is assumed that the students have significance experience with computer vision, machine learning, and image analysis.
Grading: Grading is P/F unless a student specifically request otherwise.
Course Outline
See the paper list below for the full paper citations. I just list the authors here.| Date | 11-12 | Speaker 1 | 12-1 | Speaker 2 |
| 8/30 | Ifeoma talk. | Ifeoma | Introduction | |
| 9/6 | No Meeting | |||
| 9/13 | Research Talk | Jeff | ||
| 9/20 | Grundman et al. CVPR 10 | Sagar | Research Talk | Jason |
| 9/27 | Fei Fei et al. BMVC 05 | Kushal | Research Talk | Caiming |
| 10/4 | Laptev et al. CVPR 08 | Duygu | No Meeting | |
| 10/11 | Badrinarayanan et al. CVPR 10 | Utkarsh | Rav-Acha et al. CVPR 06 | Kevin |
| 10/18 | Savarese et al. MVC 08 | Xin | Research Talk | Gang |
| 10/25 | Bai et al. SIGGRAPH 08 | Albert | Sun et al. CVPR 2009 | Caiming |
| 11/1 | Ross et al. NIPS 05 | Aishwarya | CVPR Round-Up | All |
| 11/8 | No Meeting | No Meeting | ||
| 11/10 CVPR DEADLINE | ||||
| 11/15 | Bobick and Davis PAMI 2001 | Ananth | Blank et al. ICCV 05 | Jeff |
| 11/22 BREAK WEEK | ||||
| 11/29 | Zhou et al. NIPS 06 | Kushal | Research Talk | Avik |
| 12/6 | Zhu and Mumford FTCGV 07 | Avik | Research Talk | Sagar |
The paper list was circulated in class. This is a partial list and can be augmented by participants.
- Video Segmentation
- S. Paris. Edge-preserving smoothing and mean-shift segmentation of video streams. In ECCV, 2008.
- W. Brendel and S. Todorovic. Video object segmentation by tracking regions. In ICCV, 2009.
- Y. Huang, Q. Liu, and D. Metaxas. Video object segmentation by hypergraph cut. In CVPR, 2009.
- M. Grundmann, V. Kwatra, M. Han, I. Essa, Efficient Hierarchical Graph-Based Video Segmentation, cvpr 2010
- Video Segmentation (Interactive)
- B. Price, B. Morse, and S. Cohen. Livecut: Learning-based interactive video segmentation by evaluation of multiple propagated cues. In ICCV, 2009.
- X. Bai, J. Wang, D. Simons, and G. Sapiro. Video snapcut: robust video object cutout using localized classifiers. ACM SIGGRAPH, 28, 2009.
- Spatiotemporal Interest Points
- I. Laptev and T. Lindeberg. Space-time Interest Points. ICCV 2003.
- P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. VS-PETS 2005.
- Y. Ke, R. Sukthankar, and M. Hebert. Efficient Visual Event Detection using Volumetric Features. ICCV 2005.
- A. Oikonomopoulos, I. Patras, and M. Pantic. Spatiotemporal Salient Points for Visual Recognition of Human Actions. SMC-B 36(3):710-719. 2006.
- I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, pages 1–8, Anchorage, Alaska, June 2008.
- Activity/Motion Recognition/Learning
- R. Polana and R. C. Nelson. Detecting activities. CVPR 1993
- A. Madabhushi and J. K. Aggarwal. A bayesian approach to human activity recognition. In VS ’99: Workshop on Visual Surveillance, page 25, 1999.
- A. F. Bobick and J. W. Davis. The recognition of human movement using temporal templates. IEEE PAMI, 23:257– 267, 2001.
- C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: A local svm approach. In ICPR, pages 32–36, 2004.
- M. Blank, L. Gorelick, E. Shechtman, M. Irani, and R. Basri. Actions as Space-Time Shapes. ICCV 2005. (or PAMI Version)
- A. Bissacco and S. Soatto. Classifying Human Dynamics Without Contact Forces. CVPR 2006.
- T. T. Truyen, D. Q. Phung, S. Venkatesh and H. H. Bui. AdaBoost.MRF: Boosted Markov Random Forests and Application to Multilevel Activity Recognition. CVPR 2006.
- A. Veeraraghavan, R. Chellappa and A.K. Roy-Chowdhury. The Function Space of an Activity. CVPR 2006.
- J. C. Niebles and L. Fei-Fei. A hierarchical model of shape and appearance for human action classification. CVPR 2007.
- E. Shechtman and M. Irani. Space-time behavior based correlation -- OR -- How to tell if two underlying motion fields are similar without computing them? PAMI 2007. 29(11):2045-2056.
- H. Jiang and D. R. Martin. Finding actions using shape flows. ECCV 2008.
- J. Sun, X. Wu, S. Yan, L.-F. Cheong, T.-S. Chua, and J. Li. Hierarchical spatio-temporal context modeling for action recognition. In CVPR, 2009.
- R. Messing, C. Pal, and H. Kautz. Activity recognition using the velocity histories of tracked keypoints. ICCV 2009.
- Unsupervised Action Analysis
- J. C. Niebles, H. Wang. and L. Fei-Fei. Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. BMVC 2005.
- G. Mori, H. Jiang, M. S. Drew, Y. Wang. Unsupervised Discovery of Action Classes. CVPR 2006.
- S. Savarese, A. D. Pozo, J. C. Niebles, and L. Fei-Fei. Spatial-temporal correlations for unsupervised action classification. In Motion and Video Computing, 2008.
- Video Summarization (only a few)
- H.-W. Kang, Y. Matsushita, X. Tang and X.-Q. Chen. Space-Time Video Montage. CVPR 2006.
- A. Rav-Acha, Y. Pritch and S. Peleg. Making a Long Video Short: Dynamic Video Synopsis. CVPR 2006.
- Technical Background Papers
- D. Zhou, J. Huang, and B. Sch"okopf. Learning with hypergraphs: Clustering, classification, and embedding. In NIPS’06
- R. Zass and A. Shashua. Probabilistic graph and hypergraph matching. In CVPR 2008
- D. Freedman and P. Kisilev. Fast mean shift by compact density representation. In CVPR 2009.
- D. Ross, J. Lim, R.-S. Lin, and M.-H. Yang. Incremental Learning for Robust Visual Tracking. In NIPS 2005.