Selected Space-Time Based Methods for Action Recognition

1. Jain, M., Gemert, J.C., Snoek, C.G.M.: What do 15,000 object categories tell us about classifying and localizing actions? In: CVPR, pp. 46–55 (2015)

   Google Scholar 

2. Peng, X., Zou, C.Q., Qiao, Y., Peng, Q.: Action recognition with stacked fisher vectors. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 581–595. Springer, Heidelberg (2014)

   Google Scholar 

3. Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: CVPR, pp. 4305–4314 (2015)

   Google Scholar 

4. Ni, B., Moulin, P., Yang, X., Yan, S.: Motion part regularization: improving action recognition via trajectory group selection. In: CVPR, pp. 3698–3706 (2015)

   Google Scholar 

5. Lan, Z., Lin, X., Li, X., Hauptmann, A.G., Raj, B.: Beyond gaussian pyramid: multi-skip feature stacking for action recognition. In: CVPR, pp. 204–212 (2015)

   Google Scholar 

6. Fernando, B., Gavves, E., Oramas, J., Ghodrati, A., Tuytelaars, T.: Modeling video evolution for action recognition. In: CVPR, pp. 5378–5387 (2015)

   Google Scholar 

7. Shi, F., Laganiere, R., Petriu, E.: Gradient boundary histograms for action recognition. In: WACV, pp. 1107–1114 (2015)

   Google Scholar 

8. Peng, X., Wang, L., Wang, X., Qiao, Y.: Bag of visual words and fusion methods for action recognition: comprehensive study and good practice. In: arXiv preprint arxiv:1405.4506 [cs.CV] (2014)

9. Wang, H., Oneata, D., Verbeek, J., Schmid, C.: A robust and efficient video representation for action recognition. In: arXiv preprint arxiv:1504.05524 [cs.CV] (2015)

   Google Scholar 

10. Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS, pp. 568–576 (2014)

    Google Scholar 

11. Wang, H., Schmid, C.: Action recognition with improved trajectories. In: ICCV, pp. 3551–3558 (2013)

    Google Scholar 

12. Liu, L., Shao, L., Li, X., Lu, K.: Learning spatio-temporal representations for action recognition: a genetic programming approach. IEEE Trans. Cybern. 46, 158–170 (2015)

    Article  Google Scholar 

13. Wang, H., Kläser, A., Schmid, C., Liu, C.-L.: Action recognition by dense trajectories. In: CVPR, pp. 3169–3176 (2011)

    Google Scholar 

14. Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: Towards good practices for very deep two-stream ConvNets. In: arXiv preprint arxiv:1507.02159 [cs.CV] (2015)

15. Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: arXiv preprint arxiv:1412.0767 [cs.CV] (2015)

16. Xu, Z., Yang, Y., Hauptmann, A.G.: A discriminative CNN video representation for event detection. In: CVPR, pp. 1798–1807 (2015)

    Google Scholar 

17. Xu, Z., Zhu, L., Yang, Y., Hauptmann, A.G.: UTS-CMU at THUMOS2015. In: THUMOS challenge 2015 (2015)

    Google Scholar 

18. Gorban, A., Idrees, H., Jiang, Y.-G., Roshan Zamir, A., Laptev, I., Shah, M., Sukthankar, R.: THUMOS challenge: action recognition with large number of classes (2015). http://www.thumos.info/

19. Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43(3), 16 (2011)

    Article  Google Scholar 

20. Ke, S.-R., Thuc, H.L.U., Lee, Y.-J., Hwang, J.-N., Yoo, J.-H., Choi, K.-H.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)

    Article  Google Scholar 

21. Cheng, G., Wan, Y., Saudagar, A.N., Namuduri, K., Buckles, B.P.: Advances in human action recognition: a survey. In: arXiv preprint arxiv:1501.05964 [cs.CV] (2015)

22. Wang, H., Ullah, M.M., Kläser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC, pp. 124.1–124.11 (2009)

    Google Scholar 

23. Uijlings, J., Duta, I.C., Sangineto, E., Sebe, N.: Video classification with densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off. IJMIR 4(1), 33–44 (2014)

    Google Scholar 

24. Oneata, D., Verbeek, J., Schmid, C.: Action and event recognition with fisher vectors on a compact feature set. In: ICCV, pp. 1817–1824 (2013)

    Google Scholar 

25. Kantorov, V., Laptev, I.: Efficient feature extraction, encoding and classification for action recognition. In: CVPR, pp. 2593–2600 (2014)

    Google Scholar 

26. Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV, pp. 432–439 (2003)

    Google Scholar 

27. Shi, F., Petriu, E.M., Cordeiro, A.: Human action recognition from local part model. In: Proceedings of the IEEE International Haptic Audio Visual Environments and Games (HAVE) Workshop, pp. 35–38 (2011)

    Google Scholar 

28. Shi, F., Petriu, E., Laganiere, R.: Sampling strategies for real-time action recognition. In: CVPR, pp. 2595–2602 (2013)

    Google Scholar 

29. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, T., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: CVPR, pp. 1725–1732 (2014)

    Google Scholar 

30. Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: ICCV, pp. 1550–5499 (2011)

    Google Scholar 

31. Marszalek, M., Laptev, I., Schmid, C.: Actions in context. In: CVPR, pp. 2929–2936 (2009)

    Google Scholar 

32. Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. In: arXiv preprint arxiv:1212.0402 [cs.CV] (2012)

33. Berg, A., Deng, J., Fei-Fei, L.: Large scale visual recognition challenge (ILSVRC) (2010). http://www.image-net.org/challenges/LSVRC/2010

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