Abstract
Scene understanding approaches are mainly based on geometric information, not considering the behavior of humans. The proposed approach introduces a novel human-centric scene understanding approach, based on long-term tracking information. Long-term tracking information is filtered, clustered and areas offering meaningful functionalities for humans are modeled using a kernel density estimation. This approach allows to model walking and sitting areas within an indoor scene without considering any geometric information. Thus, it solely uses continuous and noisy tracking data, acquired from a 3D sensor, monitoring the scene from a bird’s eye view. The proposed approach is evaluated on three different datasets from two application domains (home and office environment), containing more than 180 days of tracking data.
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Since Primesense is not supporting the OpenNI project any longer, the authors would like to stress that the proposed approach is fully independent from third party companies. Hence, other depth cameras and tracking algorithms can be used in order to obtain the long-term tracking data.
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References
OpenNI (2011). http://www.openni.org. Accessed 10 April 2014
Delaitre, V., Fouhey, D.F., Laptev, I., Sivic, J., Gupta, A., Efros, A.A.: Scene semantics from long-term observation of people. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 284–298. Springer, Heidelberg (2012)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. PAMI 32(9), 1627–1645 (2010)
Fouhey, D.F., Delaitre, V., Gupta, A., Efros, A.A., Laptev, I., Sivic, J.: People watching: human actions as a cue for single view geometry. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 732–745. Springer, Heidelberg (2012)
Gupta, A., Satkin, S., Efros, A.A., Hebert, M.: From 3D scene geometry to human workspace. In: CVPR, pp. 1961–1968 (2011)
Gupta, S., Arbelaez, P., Malik, J.: Perceptual organization and recognition of indoor scenes from RGB-D images. In: CVPR, pp. 564–571 (2013)
Janoch, A., Karayev, S., Jia, Y., Barron, J., Fritz, M., Saenko, K., Darrell, T.: A category-level 3-d object dataset: putting the kinect to work. In: ICCV Workshop on Consumer Depth Cameras for Computer Vision, pp. 141–165 (2013)
Lu, J., Wang, G.: Human-Centric Indoor Environment Modeling from Depth Videos. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part II. LNCS, vol. 7584, pp. 42–51. Springer, Heidelberg (2012)
Mutch, J., Lowe, D.G.: Multiclass Object Recognition with Sparse, Localized Features. In: CVPR, vol. 1, 11–18 (2006)
Planinc, R., Kampel, M.: Robust fall detection by combining 3D data and fuzzy logic. In: Park, J.-I., Kim, J. (eds.) ACCV Workshops 2012, Part II. LNCS, vol. 7729, pp. 121–132. Springer, Heidelberg (2013)
Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor Segmentation and Support Inference from RGBD Images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 746–760. Springer, Heidelberg (2012)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from rgbd images. In: PAIR, pp. 842–849 (2011)
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: CVPR, pp. 1290–1297 (2012)
Xia, L., Chen, C., Aggarwal, J.: View invariant human action recognition using histograms of 3d joints. In: CVPR-Workshop, pp. 20–27 (2012)
Acknowledgement
This work is supported by the EU and national funding organisations of EU member states (AAL 2013-6-063).
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Planinc, R., Kampel, M. (2015). Human Centered Scene Understanding Based on 3D Long-Term Tracking Data. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_11
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DOI: https://doi.org/10.1007/978-3-319-19390-8_11
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