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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-16634-6_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16633-9
Online ISBN: 978-3-319-16634-6
eBook Packages: Computer ScienceComputer Science (R0)