Déjà vu: Motion prediction in static images

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8691 LNCS, Issue PART 3, 2014, Pages 172-187

  Pintea, Silvia L ^a   Van Gemert, Jan C ^a   Smeulders, Arnold W M ^a  

^a UNIVERSITY OF AMSTERDAM   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION TREES;

ACTION RECOGNITION; FIRST PRINCIPLES; LOCAL MOTIONS; MOTION PREDICTION; MOTION SALIENCIES; RANDOM FORESTS; STATIC IMAGES; UNEXPECTED EVENTS;

FORECASTING;

EID: 84906491151     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10578-9_12     Document Type: Conference Paper

References (31)

1. Breiman, L.: Random forests. In: Machine learning (2001)
2. Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 611-625. Springer, Heidelberg (2012)
3. Cabral, B., Leedom, L.C.: Imaging vector fields using line integral convolution. In: Computer Graphics and Interactive Techniques (1993)
4. Cifuentes, C.G., Sturzel, M., Jurie, F., Brostow, G.J.: et al.: Motion models that only work sometimes. In: BMVC (2012)
5. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. In: Foundations and Trends® in Computer Graphics and Vision (2012)
6. Dai, S., Wu, Y.: Motion from blur. In: CVPR (2008)
7. Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 428-441. Springer, Heidelberg (2006) (Pubitemid 44029745)
8. Delaitre, V., Laptev, I., Sivic, J.: Recognizing human actions in still images: a study of bag-of-features and part-based representations. In: BMVC (2010)
9. Dollár, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013)
10. Everts, I., van Gemert, J., Gevers, T.: Evaluation of color stips for human action recognition. In: CVPR (2013)
11. Fanello, S., Keskin, C., Kohli, P., Izadi, S., Shotton, J., Criminisi, A., Pattaccini, U., Paek, T.: Filter forests for learning data-dependent convolutional kernels
12. Freeman, W.T., Adelson, E.H., Heeger, D.J.: Motion without movement. In: Computer Graphics (1991)
13. van Gemert, J.: Exploiting photographic style for category-level image classification by generalizing the spatial pyramid. In: ICMR (2011)
14. Hastie, T., Tibshirani, R., Friedman, J.J.H.: The elements of statistical learning (2001)
15. Ikizler-Cinbis, N., Cinbis, R., Sclaroff, S.: Learning actions from the web. In: ICCV (2009)
16. Kitani, K.M., Ziebart, B.D., Bagnell, J.A., Hebert, M.: Activity forecasting. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part IV. LNCS, vol. 7575, pp. 201-214. Springer, Heidelberg (2012)
17. Klaser, A., Marszałek, M., Schmid, C., et al.: A spatio-temporal descriptor based on 3d-gradients. In: BMVC (2008)
18. Kontschieder, P., Rota Bulò, S., Bischof, H., Pelillo, M.: Structured class-labels in random forests for semantic image labeling. In: ICCV (2011)
19. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: ICML (2001)
20. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: CVPR (2008)
21. Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR (2006)
22. Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: SIFT flow: Dense correspondence across different scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 28-42. Springer, Heidelberg (2008)
23. Max, N., Crawfis, R., Grant, C.: Visualizing 3d velocity fields near contour surfaces. In: Conference on Visualization (1994)
24. Prest, A., Leistner, C., Civera, J., Schmid, C., Ferrari, V.: Learning object class detectors from weakly annotated video. In: CVPR (2012)
25. Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: ICPR (2004)
26. Shotton, J., Sharp, T., Kohli, P., Nowozin, S., Winn, J., Criminisi, A.: Decision jungles: Compact and rich models for classification. In: NIPS (2013)
27. Smeaton, A., Over, P., Kraaij, W.: Evaluation campaigns and trecvid. In: ACM-mir (2006)
28. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y., Singer, Y.: Large margin methods for structured and interdependent output variables. In: JMLR (2006)
29. Van De Sande, K.E., Gevers, T., Snoek, C.G.: Evaluating color descriptors for object and scene recognition. In: PAMI (2010)
30. Van Gemert, J.C., Veenman, C.J., Geusebroek, J.M.: Episode-constrained cross-validation in video concept retrieval. Transactions on Multimedia (2009)
31. Wang, H., Ulla, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatiotemporal features for action recognition. In: BMVC (2009)