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Real-Time Semantic Segmentation with Label Propagation Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Human Interaction Prediction Using Deep Temporal Features

Authors : Qiuhong Ke, Mohammed Bennamoun, Senjian An, Farid Boussaid, Ferdous Sohel

Published in: Computer Vision – ECCV 2016 Workshops

Publisher: Springer International Publishing

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Abstract

Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction.

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Literature
1.
Ryoo, M.: Human activity prediction: Early recognition of ongoing activities from streaming videos. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1036–1043. IEEE (2011)
2.
Lan, T., Chen, T.-C., Savarese, S.: A hierarchical representation for future action prediction. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part III. LNCS, vol. 8691, pp. 689–704. Springer, Heidelberg (2014)
3.
Acar, E., Hopfgartner, F., Albayrak, S.: Understanding affective content of music videos through learned representations. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 303–314. Springer, Heidelberg (2014)CrossRef
4.
Le, Q.V., Zou, W.Y., Yeung, S.Y., Ng, A.Y.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3361–3368. IEEE (2011)
5.
Ren, X., Ramanan, D.: Histograms of sparse codes for object detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3246–3253. IEEE (2013)
6.
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: A deep convolutional activation feature for generic visual recognition. arXiv preprint arXiv:​1310.​1531 (2013)
7.
Oquab, M., Bottou, L., Laptev, I., Sivic, J.: Learning and transferring mid-level image representations using convolutional neural networks. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1717–1724. IEEE (2014)
8.
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Heidelberg (2014)
9.
Razavian, A.S., Azizpour, H., Sullivan, J., Carlsson, S.: Cnn features off-the-shelf: an astounding baseline for recognition. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 512–519. IEEE (2014)
10.
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. Int. J. Comput. Vis., 1–42 (2014)
11.
Patron-Perez, A., Marszalek, M., Zisserman, A., Reid, I.D.: High five: Recognising human interactions in tv shows. In: BMVC, vol. 1, p. 2, Citeseer (2010)
12.
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:​1405.​3531 (2014)
13.
Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities. In: 2009 IEEE 12th International Conference on Computer vision, pp. 1593–1600. IEEE (2009)
14.
Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. Int. J. Comput. Vision 61(3), 211–231 (2005)CrossRef
15.
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)CrossRef
16.
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. Int. J. Comput. Vision 92(1), 1–31 (2011)CrossRef
17.
Liu, C.: Beyond pixels: exploring new representations and applications for motion analysis. Ph.D. thesis, Citeseer (2009)
18.
19.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807–814 (2010)
20.
Ess, A., Leibe, B., Schindler, K., Gool, L.V.: A mobile vision system for robust multi-person tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. pp. 1–8. IEEE (2008)
21.
Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Proceedings of COMPSTAT 2010, pp. 177–186 (2010)
22.
Laviers, K., Sukthankar, G., Aha, D.W., Molineaux, M., Darken, C., et al.: Improving offensive performance through opponent modeling. In: AIIDE (2009)
Metadata
Title
Human Interaction Prediction Using Deep Temporal Features
Authors
Qiuhong Ke
Mohammed Bennamoun
Senjian An
Farid Boussaid
Ferdous Sohel
Copyright Year
2016
Book
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-48880-6

Electronic ISBN: 978-3-319-48881-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-48881-3_28

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