Abstract
With the increasing availability of wearable cameras, research on first-person view videos (egocentric videos) has received much attention recently. While some effort has been devoted to collecting various egocentric video datasets, there has not been a focused effort in assembling one that could capture the diversity and complexity of activities related to life-logging, which is expected to be an important application for egocentric videos. In this work, we first conduct a comprehensive survey of existing egocentric video datasets. We observe that existing datasets do not emphasize activities relevant to the life-logging scenario. We build an egocentric video dataset dubbed LENA (Life-logging EgoceNtric Activities) (http://people.sutd.edu.sg/~1000892/dataset) which includes egocentric videos of 13 fine-grained activity categories, recorded under diverse situations and environments using the Google Glass. Activities in LENA can also be grouped into 5 top-level categories to meet various needs and multiple demands for activities analysis research. We evaluate state-of-the-art activity recognition using LENA in detail and also analyze the performance of popular descriptors in egocentric activity recognition.
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References
Microsoft research sensecam. http://research.microsoft.com/sensecam (2013)
Autographer. http://www.autographer.com/ (2013)
Vicon revue wearable cameras. http://viconrevue.com/ (2013)
Ren, X., Gu, C.: Figure-ground segmentation improves handled object recognition in egocentric video. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3137–3144. IEEE (2010)
Fathi, A., Ren, X., Rehg, J.M.: Learning to recognize objects in egocentric activities. In: 2011 IEEE Conference On Computer Vision and Pattern Recognition (CVPR), pp. 3281–3288. IEEE (2011)
Fathi, A., Li, Y., Rehg, J.M.: Learning to recognize daily actions using gaze. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part I. LNCS, vol. 7572, pp. 314–327. Springer, Heidelberg (2012)
Alletto, S., Serra, G., Calderara, S., Solera, F., Cucchiara, R.: From ego to nos-vision: detecting social relationships in first-person views. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 580–585 (2014)
Ryoo, M.S., Matthies, L.: First-person activity recognition: what are they doing to me? In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2730–2737. IEEE (2013)
Fathi, A., Hodgins, J.K., Rehg, J.M.: Social interactions: a first-person perspective. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1226–1233. IEEE (2012)
CMU: Multi-modal activity database. http://kitchen.cs.cmu.edu/index.php (2010)
Kitani, K.M., Okabe, T., Sato, Y., Sugimoto, A.: Fast unsupervised ego-action learning for first-person sports videos. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3241–3248. IEEE (2011)
Aghazadeh, O., Sullivan, J., Carlsson, S.: Novelty detection from an ego-centric perspective. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3297–3304. IEEE (2011)
Pirsiavash, H., Ramanan, D.: Detecting activities of daily living in first-person camera views. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE (2012)
Ogaki, K., Kitani, K.M., Sugano, Y., Sato, Y.: Coupling eye-motion and ego-motion features for first-person activity recognition. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–7. IEEE (2012)
Lee, Y.J., Ghosh, J., Grauman, K.: Discovering important people and objects for egocentric video summarization. CVPR 1, 2–3 (2012)
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103, 60–79 (2013)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(27), 1–27:27 (2011). http://www.csie.ntu.edu.tw/cjlin/libsvm
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Song, S., Chandrasekhar, V., Cheung, NM., Narayan, S., Li, L., Lim, JH. (2015). Activity Recognition in Egocentric Life-Logging Videos. In: Jawahar, C., Shan, S. (eds) Computer Vision - ACCV 2014 Workshops. ACCV 2014. Lecture Notes in Computer Science(), vol 9010. Springer, Cham. https://doi.org/10.1007/978-3-319-16634-6_33
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DOI: https://doi.org/10.1007/978-3-319-16634-6_33
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