The THUMOS challenge on action recognition for videos “in the wild”

Computer Vision and Image Understanding

Volumn 155, Issue , 2017, Pages 1-23

  Idrees, Haroon ^a   Zamir, Amir R ^b   Jiang, Yu Gang ^c   Gorban, Alex ^e   Laptev, Ivan ^d   Sukthankar, Rahul ^e   Shah, Mubarak ^a  

^a UNIVERSITY OF CENTRAL FLORIDA   (United States)

^b Stanford University   (United States)

^c FUDAN UNIVERSITY   (China)

^d Centre National de la Recherche Scientifique   (France)

^e GOOGLE INC   (United States)

Author keywords

Action detection; Action localization; Action recognition; Benchmark; Dataset; THUMOS; UCF101; Untrimmed videos

Indexed keywords

BENCHMARKING; IMAGE UNDERSTANDING;

ACTION LOCALIZATION; ACTION RECOGNITION; DATASET; THUMOS; UCF101; UNTRIMMED VIDEOS;

SOFTWARE ENGINEERING;

EID: 85006106345     PISSN: 10773142     EISSN: 1090235X     Source Type: Journal    
DOI: 10.1016/j.cviu.2016.10.018     Document Type: Article

References (64)

1. Ayache, S., Quénot, G., Video corpus annotation using active learning. European Conference on Information Retrieval, 2008, Springer, 187–198.
2. Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R., Actions as space-time shapes. IEEE ICCV, 2005.
3. Bojanowski, P., Lajugie, R., Bach, F., Laptev, I., Ponce, J., Schmid, C., Sivic, J., Weakly supervised action labeling in videos under ordering constraints. ECCV, 2014.
4. Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J., Activitynet: a large-scale video benchmark for human activity understanding. IEEE CVPR, 2015.
5. Cai, J., Tian, Q., UTSA submission to THUMOS 2015. THUMOS’15 Action Recognition Challenge, 2015.
6. Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A., Return of the devil in the details: Delving deep into convolutional nets. BMVC, 2014.
7. Duchenne, O., Laptev, I., Sivic, J., Bach, F., Ponce, J., Automatic annotation of human actions in video. IEEE ICCV, 2009.
8. Gan, C., Sun, C., Kovvuri, R., Nevatia, R., USC & THU at THUMOS 2015. THUMOS’15 Action Recognition Challenge, 2015.
9. Gorban, A., Idrees, H., Jiang, Y.-G., Roshan Zamir, A., Laptev, I., Shah, M., Sukthankar, R., THUMOS challenge: action recognition with a large number of classes. 2015 http://www.thumos.info/.
10. Hoai, M., Lan, Z.-Z., De la Torre, F., Joint segmentation and classification of human actions in video. IEEE CVPR, 2011.
11. Jain, M., van Gemert, J.C., Mettes, P., Snoek, C.G., ISLA, I., University of Amsterdam at THUMOS 2015. THUMOS’15 Action Recognition Challenge, 2015.
12. Jégou, H., Douze, M., Schmid, C., Pérez, P., Aggregating local descriptors into a compact image representation. IEEE CVPR, 2010.
13. Jiang, Y.-G., Liu, J., Zamir, A.R., Laptev, I., Piccardi, M., Shah, M., Sukthankar, R., THUMOS’13: ICCV workshop on action recognition with a large number of classes. 2013 http://crcv.ucf.edu/ICCV13-Action-Workshop/.
14. Jiang, Y.-G., Liu, J., Zamir, A.R., Toderici, G., Laptev, I., Shah, M., Sukthankar, R., THUMOS’14: ECCV workshop on action recognition with a large number of classes. 2014 http://crcv.ucf.edu/THUMOS14/.
15. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L., Large-scale video classification with convolutional neural networks. IEEE CVPR, 2014.
16. Ke, Y., Sukthankar, R., Hebert, M., Efficient visual event detection using volumetric features. IEEE ICCV, 2005.
17. Ke, Y., Sukthankar, R., Hebert, M., Event detection in crowded videos. IEEE ICCV, 2007.
18. Klaser, A., Marszałek, M., Schmid, C., Zisserman, A., Human focused action localization in video. International Workshop on Sign, Gesture, and Activity (SGA), ECCV Workshops, 2010.
19. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T., HMDB: a large video database for human motion recognition. IEEE ICCV, 2011.
20. Lan, Z., Lin, M., Li, X., Hauptmann, A.G., Raj, B., Beyond gaussian pyramid: multi-skip feature stacking for action recognition. IEEE CVPR, 2015.
21. Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B., Learning realistic human actions from movies. IEEE CVPR, 2008.
22. Laptev, I., Pérez, P., Retrieving actions in movies. IEEE ICCV, 2007.
23. Liu, J., Luo, J., Shah, M., Recognizing realistic actions from videos “in the wild”. IEEE CVPR, 2009.
24. Liu, Y., Fan, B., Zhao, S., Xu, Y., Han, Y., Tianjin University submission at THUMOS challenge 2015. THUMOS’15 Action Recognition Challenge, 2015.
25. Marszałek, M., Laptev, I., Schmid, C., Actions in context. IEEE CVPR, 2009.
26. Niebles, J.C., Chen, C.-W., Li, F.-F., Modeling temporal structure of decomposable motion segments for activity classification. ECCV, 2010.
27. Ning, K., Wu, F., ZJUDCD submission at THUMOS challenge 2015. THUMOS’15 Action Recognition Challenge, 2015.
28. Ohnishi, K., Harada, T., Mil-utokyo at thumos challenge 2015. THUMOS’15 Action Recognition Challenge, 2015.
29. Oneata, D., Verbeek, J., Schmid, C., Action and event recognition with Fisher vectors on a compact feature set. IEEE ICCV, 2013.
30. Oneata, D., Verbeek, J., Schmid, C., Efficient action localization with approximately normalized fisher vectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014.
31. Peng, X., Schmid, C., Encoding feature maps of CNNs for action recognition. THUMOS’15 Action Recognition Challenge, 2015.
32. Perronnin, F., Sánchez, J., Mensink, T., Improving the fisher kernel for large-scale image classification. ECCV, 2010.
33. Pirsiavash, H., Ramanan, D., Detecting activities of daily living in first-person camera views. IEEE CVPR, 2012.
34. Pirsiavash, H., Ramanan, D., Parsing videos of actions with segmental grammars. IEEE CVPR, 2014.
35. Qiu, Z., Li, Q., Yao, T., Mei, T., Rui, Y., MSR Asia MSM at THUMOS challenge 2015. THUMOS’15 Action Recognition Challenge, 2015.
36. Raptis, M., Sigal, L., Poselet key-framing: a model for human activity recognition. IEEE CVPR, 2013.
37. Reddy, K.K., Shah, M., Recognizing 50 human action categories of web videos. Mach. Vis Appl. 24:5 (2013), 971–981.
38. Richard, A., Gall, J., Temporal action detection using a statistical language model. 2016.
39. Rodriguez, M., Ahmed, J., Shah, M., Action MACH: a spatio-temporal maximum average correlation height filter for action recognition. IEEE CVPR, 2008.
40. Ryoo, M.S., Matthies, L., First-person activity recognition: what are they doing to me?. IEEE CVPR, 2013.
41. Sánchez, J., Perronnin, F., Mensink, T., Verbeek, J., Image classification with the fisher vector: theory and practice. IJCV 105:3 (2013), 222–245.
42. Satkin, S., Hebert, M., Modeling the temporal extent of actions. ECCV, 2010.
43. Schuldt, C., Laptev, I., Caputo, B., Recognizing human actions: a local SVM approach. ICPR, 2004.
44. Shou, Z., Wang, D., Chang, S.-F., Action temporal localization in untrimmed videos via multi-stage cnns. IEEE CVPR, 2016.
45. Simonyan, K., Zisserman, A., Two-stream convolutional networks for action recognition in videos. NIPS, 2014.
46. Simonyan, K., Zisserman, A., Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
47. Soomro, K., Idrees, H., Shah, M., Action localization in videos through context walk. IEEE ICCV, 2015.
48. Soomro, K., Idrees, H., Shah, M., Predicting the where and what of actors and actions through online action localization. CVPR, 2016.
49. Soomro, K., Zamir, A.R., Shah, M., UCF101: A Dataset of 101 Human Action Classes from Videos in the Wild. Technical Report CRCV-TR-12-01, 2012, UCF.
50. Strassel, S., Morris, A., Fiscus, J., Caruso, C., Lee, H., Over, P., Fiumara, J., Shaw, B., Antonishek, B., Michel, M., Creating havic: Heterogeneous audio visual internet collection. Chair), N.C.C., Choukri, K., Declerck, T., Doan, M.U., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., (eds.) Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), 2012, European Language Resources Association (ELRA), Istanbul, Turkey.
51. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., Going deeper with convolutions. IEEE CVPR, 2015.
52. Tang, K., Fei-Fei, L., Koller, D., Learning latent temporal structure for complex event detection. IEEE CVPR, 2012.
53. Tian, Y., Sukthankar, R., Shah, M., Spatiotemporal deformable part models for action detection. IEEE CVPR, 2013.
54. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M., Learning spatiotemporal features with 3d convolutional networks. ICCV, 2015.
55. Wang, H., Schmid, C., Action recognition with improved trajectories. IEEE ICCV, 2013.
56. Wang, L., Wang, Z., Xiong, Y., Qiao, Y., CUHK&SIAT submission for THUMOS’15 action recognition challenge. THUMOS’15 Action Recognition Challenge, 2015.
57. Weinland, D., Boyer, E., Ronfard, R., Action recognition from arbitrary views using 3d exemplars. 2007 IEEE 11th International Conference on Computer Vision, 2007, IEEE, 1–7.
58. Xu, Z., Yang, Y., Hauptmann, A.G., A discriminative cnn video representation for event detection. IEEE CVPR, 2015.
59. Xu, Z., Zhu, L., Yang, Y., Hauptmann, A., UTS-CMU at THUMOS 2015. THUMOS’15 Action Recognition Challenge, 2015.
60. Yilmaz, E., Aslam, J.A., Estimating average precision with incomplete and imperfect judgments. Proceedings of the 15th ACM International Conference on Information and Knowledge Management, 2006, ACM.
61. Yu, S.-I., Jiang, L., Mao, Z., Chang, X., Du, X., Gan, C., Lan, Z., Xu, Z., Li, X., Cai, Y., et al. Informedia@ trecvid 2014 med and mer. NIST TRECVID Video Retrieval Evaluation Workshop, 2014.
62. Yuan, J., Liu, Z., Wu, Y., Discriminative subvolume search for efficient action detection. IEEE CVPR, 2009.
63. Yuan, J., Pei, Y., Ni, B., Moulin, P., Kassim, A., Adsc submission at thumos challenge 2015. THUMOS’15 Action Recognition Challenge, 2015.
64. Zeiler, M.D., Fergus, R., Visualizing and understanding convolutional networks. ECCV, 2014.