Multi-region Two-Stream R-CNN for Action Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9908)

Abstract

We propose a multi-region two-stream R-CNN model for action detection in realistic videos. We start from frame-level action detection based on faster R-CNN, and make three contributions: (1) we show that a motion region proposal network generates high-quality proposals, which are complementary to those of an appearance region proposal network; (2) we show that stacking optical flow over several frames significantly improves frame-level action detection; and (3) we embed a multi-region scheme in the faster R-CNN model, which adds complementary information on body parts. We then link frame-level detections with the Viterbi algorithm, and temporally localize an action with the maximum subarray method. Experimental results on the UCF-Sports, J-HMDB and UCF101 action detection datasets show that our approach outperforms the state of the art with a significant margin in both frame-mAP and video-mAP.

Keywords

Action detection Faster R-CNN Multi-region CNNs Two stream R-CNN 

Notes

Acknowledgments

This work was supported in part by the ERC advanced grant ALLEGRO, the MSR-Inria joint project, a Google research award, a Facebook gift, the Natural Science Foundation of China (No. 61502152) and the Open Projects Program of National Laboratory of Pattern Recognition. We gratefully acknowledge the support of NVIDIA with the donation of GPUs used for this research.

References

  1. 1.
    Wang, H., Schmid, C.: Action recognition with improved trajectories. In: ICCV, pp. 3551–3558 (2013)Google Scholar
  2. 2.
    Jain, A., Gupta, A., Rodriguez, M., Davis, L.: Representing videos using mid-level discriminative patches. In: CVPR, pp. 2571–2578 (2013)Google Scholar
  3. 3.
    Peng, X., Zou, C., 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
  4. 4.
    Wang, L., Qiao, Y., Tang, X.: Action recognition with trajectory-pooled deep-convolutional descriptors. In: CVPR, pp. 4305–4314 (2015)Google Scholar
  5. 5.
    Tian, Y., Sukthankar, R., Shah, M.: Spatiotemporal deformable part models for action detection. In: CVPR, pp. 2642–2649 (2013)Google Scholar
  6. 6.
    Wang, L., Qiao, Y., Tang, X.: Video action detection with relational dynamic-poselets. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 565–580. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Gkioxari, G., Malik, J.: Finding action tubes. In: CVPR, pp. 759–768 (2015)Google Scholar
  8. 8.
    Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Learning to track for spatio-temporal action localization. In: ICCV, pp. 3164–3172 (2015)Google Scholar
  9. 9.
    Gaidon, A., Harchaoui, Z., Schmid, C.: Temporal localization of actions with actoms. PAMI 35(11), 2782–2795 (2013)CrossRefGoogle Scholar
  10. 10.
    Oneata, D., Verbeek, J., Schmid, C.: Efficient action localization with approximately normalized Fisher vectors. In: CVPR, pp. 2545–2552 (2014)Google Scholar
  11. 11.
    Escalera, S., et al.: ChaLearn looking at people challenge 2014: dataset and results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8925, pp. 459–473. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16178-5_32 CrossRefGoogle Scholar
  12. 12.
    Felzenszwalb, P., McAllester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: CVPR, pp. 1–8 (2008)Google Scholar
  13. 13.
    Bourdev, L., Malik, J.: Poselets: body part detectors trained using 3D human pose annotations. In: ICCV, pp. 1365–1372 (2009)Google Scholar
  14. 14.
    Zitnick, C.L., Dollár, P.: Edge boxes: locating object proposals from edges. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 391–405. Springer, Heidelberg (2014)Google Scholar
  15. 15.
    Gkioxari, G., Girshick, R., Malik, J.: Contextual action recognition with R*CNN. In: ICCV, pp. 1080–1088 (2015)Google Scholar
  16. 16.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp. 580–587 (2014)Google Scholar
  17. 17.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  18. 18.
    Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware CNN model. In: ICCV, pp. 1134–1142 (2015)Google Scholar
  19. 19.
    Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. IJCV 104(2), 154–171 (2013)CrossRefGoogle Scholar
  20. 20.
    Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M.: Towards understanding action recognition. In: ICCV, pp. 3192–3199 (2013)Google Scholar
  21. 21.
    Chéron, G., Laptev, I., Schmid, C.: P-CNN: pose-based CNN features for action recognition. In: ICCV, pp. 3218–3226 (2015)Google Scholar
  22. 22.
    Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: NIPS, pp. 568–576 (2014)Google Scholar
  23. 23.
    Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. (CSUR) 43(3), 16 (2011)CrossRefGoogle Scholar
  24. 24.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  25. 25.
    Sivic, J., Zisserman, A.: Video Google: a text retrieval approach to object matching in videos. In: ICCV, pp. 1470–1477 (2003)Google Scholar
  26. 26.
    Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: CVPR, pp. 1725–1732 (2014)Google Scholar
  27. 27.
    Sun, L., Jia, K., Chan, T.H., Fang, Y., Wang, G., Yan, S.: DL-SFA: deeply-learned slow feature analysis for action recognition. In: CVPR, pp. 2625–2632 (2014)Google Scholar
  28. 28.
    Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  29. 29.
    Laptev, I., Pérez, P.: Retrieving actions in movies. In: ICCV 2007, pp. 1–8 (2007)Google Scholar
  30. 30.
    Yuan, J., Liu, Z., Wu, Y.: Discriminative subvolume search for efficient action detection. In: CVPR, pp. 2442–2449 (2009)Google Scholar
  31. 31.
    Rodriguez, M.D., Ahmed, J., Shah, M.: Action MACH a spatio-temporal maximum average correlation height filter for action recognition. In: CVPR, pp. 1–8 (2008)Google Scholar
  32. 32.
    Derpanis, K.G., Sizintsev, M., Cannons, K., Wildes, R.P.: Efficient action spotting based on a spacetime oriented structure representation. In: CVPR, pp. 1990–1997 (2010)Google Scholar
  33. 33.
    Tran, D., Yuan, J., Forsyth, D.: Video event detection: from subvolume localization to spatiotemporal path search. PAMI 36(2), 404–416 (2014)CrossRefGoogle Scholar
  34. 34.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
  35. 35.
    He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. PAMI 37(9), 1904–1916 (2015)CrossRefGoogle Scholar
  36. 36.
    Girshick, R.: Fast R-CNN. In: ICCV, pp. 1440–1448 (2015)Google Scholar
  37. 37.
    Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. Inf. Theory 13(2), 260–269 (1967)CrossRefMATHGoogle Scholar
  38. 38.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (2014)
  39. 39.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: ImageNet large scale visual recognition challenge. IJCV 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Dai, J., He, K., Sun, J.: Convolutional feature masking for joint object and stuff segmentation. In: CVPR, pp. 3992–4000 (2015)Google Scholar
  41. 41.
    Bentley, J.: Programming pearls: algorithm design techniques. Commun. ACM 27(9), 865–873 (1984)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Soomro, K., Zamir, A.R., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. arXiv:1212.0402 (2012)
  43. 43.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J.G. (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  44. 44.
    Yu, G., Yuan, J.: Fast action proposals for human action detection and search. In: CVPR, pp. 1302–1311 (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Thoth team, Laboratoire Jean KuntzmannInriaGrenobleFrance

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