ActivityNet: A large-scale video benchmark for human activity understanding

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 07-12-June-2015, Issue , 2015, Pages 961-970

  Heilbron, Fabian Caba ^a,b   Escorcia, Victor ^a,b   Ghanem, Bernard ^b   Niebles, Juan Carlos ^a  

^a UNIVERSIDAD DEL NORTE   (Colombia)

^b KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER VISION; GESTURE RECOGNITION; MOTION ESTIMATION;

ACTIVITY CLASSIFICATIONS; ACTIVITY DETECTION; ACTIVITY INSTANCES; COMPUTER VISION ALGORITHMS; DAILY LIVING; HUMAN ACTIVITIES; HUMAN-ACTION RECOGNITION; VIDEO CLASSIFICATION;

PATTERN RECOGNITION;

EID: 84959216468     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2015.7298698     Document Type: Conference Paper

References (43)

1. B. Ainsworth, W. Haskell, S. Herrmann, N. Meckes, D. Bas-sett Jr., C. Tudor-Locke, J. Greer, J. Vezina, M. Whitt-Glover, and A. Leon. 2011 compendium of physical activities: a sec-ond update of codes and met values. Medicine and Science in Sports and Exercise, 2011
2. M. Andriluka, L. Pishchulin, P. Gehler, and B. Schiele. 2d human pose estimation: New benchmark and state of the art analysis. In CVPR, June 2014
3. I. Atmosukarto, B. Ghanem, and N. Ahuja. Trajectory-based fisher kernel representation for action recognition in videos. In ICPR. IEEE, 2012
4. I. Atmosukarto, B. Ghanem, and N. Ahuja. Action recogni-tion using discriminative structured trajectory groups. 2015
5. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. In CVPR, 2009
6. V. Escorcia and J. C. Niebles. Spatio-temporal human-object interactions for action recognition in videos. In Computer Vision Workshops (ICCVW), 2013 IEEE International Con-ference on, 2013
7. FrameNet. http://framenet. icsi. berkeley. edu
8. Freebase: A community-curated database of well-known people, places, and things. https://www. freebase. com
9. L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri. Actions as space-time shapes. IEEE Transactions on Pat-tern Analysis and Machine Intelligence, 29(12):2247-2253, 2007
10. G. Guerra-Filho and Y. Aloimonos. Towards a sensorimotor WordNet SM: Closing the semantic gap. In International WordNet Conference, 2006
11. F. C. Heilbron and J. C. Niebles. Collecting and annotating human activities in web videos. In International Conference on Multimedia Retrieval, 2014
12. F. C. Heilbron, A. Thabet, J. C. Niebles, and B. Ghanem. Camera motion and surrounding scene appearance as context for action recognition. In ACCV, 2014
13. N. Ikizler-Cinbis and S. Sclaroff. Object, scene and actions: Combining multiple features for human action recognition. In ECCV, 2010
14. M. Jain, J. van Gemert, and C. Snoek. University of amster-dam at THUMOS Challenge 2014. ECCV THUMOS Chal-lenge, 2014
15. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Gir-shick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. CoRR, 2014
16. A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. Large-scale video classification with convo-lutional neural networks. In CVPR, 2014
17. A. R. Z. Khurram Soomro and M. Shah. A dataset of 101 hu-man action classes from videos in the wild. Technical report, University of Central Florida, 2012
18. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012
19. H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre. HMDB: A large video database for human motion recogni-tion. In ICCV, 2011
20. I. Laptev, M. Marszaek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. In CVPR, 2008
21. I. Lillo, A. Soto, and J. C. Niebles. Discriminative hierarchi-cal modeling of spatio-temporally composable human activ-ities. In CVPR, 2014
22. M. Marszalek, I. Laptev, and C. Schmid. Actions in context. In CVPR, 2009
23. G. A. Miller. WordNet: A Lexical Database for English. Communications of the ACM, 38:39-41, 1995
24. J. C. Niebles, C.-W. Chen, and L. Fei-Fei. Modeling tempo-ral structure of decomposable motion segments for activity classification. In ECCV, 2010
25. D. Oneata, J. Verbeek, and C. Schmid. The LEAR submis-sion at Thumos 2014. In ECCV THUMOS Challenge, 2014
26. K. Pastra and Y. Aloimonos. The minimalist grammar of action. Philosophical Transactions of the Royal Society B: Biological Sciences, 367(1585):103-117, 2012
27. X. Peng, C. Zou, Y. Qiao, and Q. Peng. Action recognition with stacked fisher vectors. In ECCV, 2014
28. F. Perronnin, J. Sánchez, and T. Mensink. Improving the fisher kernel for large-scale image classification. In ECCV, 2010
29. L. Pishchulin, M. Andriluka, and B. Schiele. Fine-grained activity recognition with holistic and pose based features. In X. Jiang, J. Hornegger, and R. Koch, editors, Pattern Recog-nition, volume 8753 of Lecture Notes in Computer Science, pages 678-689. Springer International Publishing, 2014
30. M. Rodriguez, J. Ahmed, and M. Shah. Action Mach a spatio-temporal maximum average correlation height filter for action recognition. In CVPR, 2008
31. M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele. A database for fine grained activity detection of cooking activ-ities. In CVPR, 2012
32. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. ImageNet Large Scale Visual Recognition Challenge, 2014
33. C. Schuldt, I. Laptev, and B. Caputo. Recognizing human actions: a local SVM approach. In ICPR, 2004
34. K. D. Tang, B. Yao, L. Fei-Fei, and D. Koller. Combining the right features for complex event recognition. In ICCV, 2013
35. Thumos challenge 2014. http://crcv. ucf. edu/THUMOS14, 2013
36. A. Torralba, R. Fergus, and W. T. Freeman. 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE Transactions on Pattern Analysis and Ma-chine Intelligence, 30(11):1958-1970, 2008
37. U. S. Department of Labor. American time use survey. http://www. bls. gov/tus/, 2013
38. C. Vondrick, D. Patterson, and D. Ramanan. Efficiently scal-ing up crowdsourced video annotation. International Journal of Computer Vision, 101(1):184-204, 2013
39. H. Wang and C. Schmid. Action Recognition with Improved Trajectories. In ICCV, 2013
40. H. Wang and C. Schmid. Lear-inria submission for the thu-mos workshop. In ICCV Workshop on Action Recognition with a Large Number of Classes, 2013
41. P. Wei, N. Zheng, Y. Zhao, and S.-C. Zhu. Concurrent action detection with structural prediction. In ICCV, 2013
42. J. Xiao, K. A. Ehinger, J. Hays, A. Torralba, and A. Oliva. SUN Database: Exploring a large collection of scene cate-gories. International Journal of Computer Vision, 2014
43. YouTube statistics. http://www. youtube. com/yt/press/statistics. html, 2015.