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2016 | OriginalPaper | Buchkapitel

Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding

verfasst von : Gunnar A. Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, Abhinav Gupta

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

Computer vision has a great potential to help our daily lives by searching for lost keys, watering flowers or reminding us to take a pill. To succeed with such tasks, computer vision methods need to be trained from real and diverse examples of our daily dynamic scenes. While most of such scenes are not particularly exciting, they typically do not appear on YouTube, in movies or TV broadcasts. So how do we collect sufficiently many diverse but boring samples representing our lives? We propose a novel Hollywood in Homes approach to collect such data. Instead of shooting videos in the lab, we ensure diversity by distributing and crowdsourcing the whole process of video creation from script writing to video recording and annotation. Following this procedure we collect a new dataset, Charades, with hundreds of people recording videos in their own homes, acting out casual everyday activities. The dataset is composed of 9,848 annotated videos with an average length of 30 s, showing activities of 267 people from three continents. Each video is annotated by multiple free-text descriptions, action labels, action intervals and classes of interacted objects. In total, Charades provides 27,847 video descriptions, 66,500 temporally localized intervals for 157 action classes and 41,104 labels for 46 object classes. Using this rich data, we evaluate and provide baseline results for several tasks including action recognition and automatic description generation. We believe that the realism, diversity, and casual nature of this dataset will present unique challenges and new opportunities for computer vision community.

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Literatur
1.
Zurück zum Zitat Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition (CVPR), pp. 248–255. IEEE (2009) Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Computer Vision and Pattern Recognition (CVPR), pp. 248–255. IEEE (2009)
2.
Zurück zum Zitat Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Neural Information Processing Systems (NIPS), pp. 487–495 (2014) Zhou, B., Lapedriza, A., Xiao, J., Torralba, A., Oliva, A.: Learning deep features for scene recognition using places database. In: Neural Information Processing Systems (NIPS), pp. 487–495 (2014)
3.
Zurück zum Zitat Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: Activitynet: a large-scale video benchmark for human activity understanding. In: Computer Vision and Pattern Recognition (CVPR), pp. 961–970. IEEE (2015) Caba Heilbron, F., Escorcia, V., Ghanem, B., Carlos Niebles, J.: Activitynet: a large-scale video benchmark for human activity understanding. In: Computer Vision and Pattern Recognition (CVPR), pp. 961–970. IEEE (2015)
4.
Zurück zum Zitat Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos in the wild. In: Computer Vision and Pattern Recognition (CVPR), pp. 1996–2003. IEEE (2009) Liu, J., Luo, J., Shah, M.: Recognizing realistic actions from videos in the wild. In: Computer Vision and Pattern Recognition (CVPR), pp. 1996–2003. IEEE (2009)
5.
Zurück zum Zitat 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/ 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/​
6.
Zurück zum Zitat Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732. IEEE (2014) Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., Fei-Fei, L.: Large-scale video classification with convolutional neural networks. In: Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732. IEEE (2014)
7.
Zurück zum Zitat Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: International Conference on Computer Vision (ICCV), pp. 2556–2563. IEEE (2011) Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: International Conference on Computer Vision (ICCV), pp. 2556–2563. IEEE (2011)
8.
Zurück zum Zitat Soomro, K., Roshan Zamir, A., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. In: CRCV-TR-12-01 (2012) Soomro, K., Roshan Zamir, A., Shah, M.: UCF101: a dataset of 101 human actions classes from videos in the wild. In: CRCV-TR-12-01 (2012)
9.
Zurück zum Zitat Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008) Laptev, I., Marszałek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
10.
Zurück zum Zitat Rodriguez, M.D., Ahmed, J., Shah, M.: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008) Rodriguez, M.D., Ahmed, J., Shah, M.: Action mach a spatio-temporal maximum average correlation height filter for action recognition. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2008)
11.
Zurück zum Zitat Rohrbach, A., Rohrbach, M., Tandon, N., Schiele, B.: A dataset for movie description. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2015) Rohrbach, A., Rohrbach, M., Tandon, N., Schiele, B.: A dataset for movie description. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2015)
12.
Zurück zum Zitat Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: International Conference on Pattern Recognition (ICPR), vol. 3, pp. 32–36. IEEE (2004) Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local svm approach. In: International Conference on Pattern Recognition (ICPR), vol. 3, pp. 32–36. IEEE (2004)
13.
Zurück zum Zitat Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)CrossRef Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)CrossRef
14.
Zurück zum Zitat Rohrbach, M., Amin, S., Andriluka, M., Schiele, B.: A database for fine grained activity detection of cooking activities. In: Computer Vision and Pattern Recognition (CVPR), pp. 1194–1201. IEEE (2012) Rohrbach, M., Amin, S., Andriluka, M., Schiele, B.: A database for fine grained activity detection of cooking activities. In: Computer Vision and Pattern Recognition (CVPR), pp. 1194–1201. IEEE (2012)
15.
Zurück zum Zitat Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J., Lee, H., Davis, L., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: Computer Vision and Pattern Recognition (CVPR), pp. 3153–3160. IEEE (2011) Oh, S., Hoogs, A., Perera, A., Cuntoor, N., Chen, C.C., Lee, J.T., Mukherjee, S., Aggarwal, J., Lee, H., Davis, L., et al.: A large-scale benchmark dataset for event recognition in surveillance video. In: Computer Vision and Pattern Recognition (CVPR), pp. 3153–3160. IEEE (2011)
16.
Zurück zum Zitat Kuehne, H., Arslan, A.B., Serre, T.: The language of actions: recovering the syntax and semantics of goal-directed human activities. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2014) Kuehne, H., Arslan, A.B., Serre, T.: The language of actions: recovering the syntax and semantics of goal-directed human activities. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2014)
17.
Zurück zum Zitat Rohrbach, A., Rohrbach, M., Qiu, W., Friedrich, A., Pinkal, M., Schiele, B.: Coherent multi-sentence video description with variable level of detail. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 184–195. Springer, Heidelberg (2014) Rohrbach, A., Rohrbach, M., Qiu, W., Friedrich, A., Pinkal, M., Schiele, B.: Coherent multi-sentence video description with variable level of detail. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 184–195. Springer, Heidelberg (2014)
18.
Zurück zum Zitat Marszałek, M., Laptev, I., Schmid, C.: Actions in context. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2009) Marszałek, M., Laptev, I., Schmid, C.: Actions in context. In: Computer Vision and Pattern Recognition (CVPR). IEEE (2009)
19.
Zurück zum Zitat Ferrari, V., Marín-Jiménez, M., Zisserman, A.: 2D human pose estimation in TV shows. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 128–147. Springer, Heidelberg (2009)CrossRef Ferrari, V., Marín-Jiménez, M., Zisserman, A.: 2D human pose estimation in TV shows. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds.) Statistical and Geometrical Approaches to Visual Motion Analysis. LNCS, vol. 5604, pp. 128–147. Springer, Heidelberg (2009)CrossRef
20.
Zurück zum Zitat Chen, D.L., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 190–200. Association for Computational Linguistics (2011) Chen, D.L., Dolan, W.B.: Collecting highly parallel data for paraphrase evaluation. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 190–200. Association for Computational Linguistics (2011)
21.
Zurück zum Zitat Torabi, A., Pal, C., Larochelle, H., Courville, A.: Using descriptive video services to create a large data source for video annotation research. arXiv preprint arXiv:1503.01070 (2015) Torabi, A., Pal, C., Larochelle, H., Courville, A.: Using descriptive video services to create a large data source for video annotation research. arXiv preprint arXiv:​1503.​01070 (2015)
22.
Zurück zum Zitat Gupta, A., Davis, L.S.: Objects in action: an approach for combining action understanding and object perception. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2007) Gupta, A., Davis, L.S.: Objects in action: an approach for combining action understanding and object perception. In: Computer Vision and Pattern Recognition (CVPR), pp. 1–8. IEEE (2007)
23.
Zurück zum Zitat Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: International Conference on Computer Vision (ICCV), pp. 1593–1600. IEEE (2009) Ryoo, M.S., Aggarwal, J.K.: Spatio-temporal relationship match: video structure comparison for recognition of complex human activities. In: International Conference on Computer Vision (ICCV), pp. 1593–1600. IEEE (2009)
24.
Zurück zum Zitat Tuite, K., Snavely, N., Hsiao, D.Y., Tabing, N., Popovic, Z.: Photocity: training experts at large-scale image acquisition through a competitive game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1383–1392. ACM (2011) Tuite, K., Snavely, N., Hsiao, D.Y., Tabing, N., Popovic, Z.: Photocity: training experts at large-scale image acquisition through a competitive game. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1383–1392. ACM (2011)
25.
Zurück zum Zitat Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE (2012) Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE (2012)
26.
Zurück zum Zitat Iwashita, Y., Takamine, A., Kurazume, R., Ryoo, M.S.: First-person animal activity recognition from egocentric videos. In: International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, August 2014 Iwashita, Y., Takamine, A., Kurazume, R., Ryoo, M.S.: First-person animal activity recognition from egocentric videos. In: International Conference on Pattern Recognition (ICPR), Stockholm, Sweden, August 2014
27.
Zurück zum Zitat Zitnick, C., Parikh, D.: Bringing semantics into focus using visual abstraction. In: Computer Vision and Pattern Recognition (CVPR), pp. 3009–3016. IEEE (2013) Zitnick, C., Parikh, D.: Bringing semantics into focus using visual abstraction. In: Computer Vision and Pattern Recognition (CVPR), pp. 3009–3016. IEEE (2013)
28.
Zurück zum Zitat Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval, pp. 24–51 (1983) Salton, G., Mcgill, M.J.: Introduction to Modern Information Retrieval, pp. 24–51 (1983)
29.
Zurück zum Zitat Sigurdsson, G.A., Russakovsky, O., Farhadi, A., Laptev, I., Gupta, A.: Much ado about time: exhaustive annotation of temporal data. arXiv preprint arXiv:1607.07429 (2016) Sigurdsson, G.A., Russakovsky, O., Farhadi, A., Laptev, I., Gupta, A.: Much ado about time: exhaustive annotation of temporal data. arXiv preprint arXiv:​1607.​07429 (2016)
30.
Zurück zum Zitat Zipf, G.K.: The psycho-biology of language (1935) Zipf, G.K.: The psycho-biology of language (1935)
31.
Zurück zum Zitat Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Ann. Rev. Neurosci. 24(1), 1193–1216 (2001)CrossRef Simoncelli, E.P., Olshausen, B.A.: Natural image statistics and neural representation. Ann. Rev. Neurosci. 24(1), 1193–1216 (2001)CrossRef
32.
Zurück zum Zitat Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2579–2605), 85 (2008)MATH Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2579–2605), 85 (2008)MATH
33.
Zurück zum Zitat Wang, H., Schmid, C.: Action recognition with improved trajectories. In: International Conference on Computer Vision (ICCV) (2013) Wang, H., Schmid, C.: Action recognition with improved trajectories. In: International Conference on Computer Vision (ICCV) (2013)
34.
Zurück zum Zitat 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)CrossRef 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)CrossRef
35.
Zurück zum Zitat Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015) Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)
36.
Zurück zum Zitat Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems (NIPS) (2012) Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Neural Information Processing Systems (NIPS) (2012)
37.
Zurück zum Zitat Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Neural Information Processing Systems (NIPS) (2014) Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Neural Information Processing Systems (NIPS) (2014)
38.
Zurück zum Zitat Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: International Conference on Computer Vision (ICCV) (2015) Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3D convolutional networks. In: International Conference on Computer Vision (ICCV) (2015)
39.
Zurück zum Zitat Chen, X., Fang, H., Lin, T., Vedantam, R., Gupta, S., Doll, P., Zitnick, C.L.: Microsoft coco captions: data collection and evaluation server. arXiv:1504.00325 (2015) Chen, X., Fang, H., Lin, T., Vedantam, R., Gupta, S., Doll, P., Zitnick, C.L.: Microsoft coco captions: data collection and evaluation server. arXiv:​1504.​00325 (2015)
40.
Zurück zum Zitat Devlin, J., Gupta, S., Girshick, R., Mitchell, M., Zitnick, C.L.: Exploring nearest neighbor approaches for image captioning. arXiv preprint arXiv:1505.04467 (2015) Devlin, J., Gupta, S., Girshick, R., Mitchell, M., Zitnick, C.L.: Exploring nearest neighbor approaches for image captioning. arXiv preprint arXiv:​1505.​04467 (2015)
41.
Zurück zum Zitat Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence-video to text. In: International Conference on Computer Vision (ICCV), pp. 4534–4542 (2015) Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., Saenko, K.: Sequence to sequence-video to text. In: International Conference on Computer Vision (ICCV), pp. 4534–4542 (2015)
Metadaten
Titel
Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding
verfasst von
Gunnar A. Sigurdsson
Gül Varol
Xiaolong Wang
Ali Farhadi
Ivan Laptev
Abhinav Gupta
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46448-0_31