Abstract
Improving human action recognition in videos is restricted by the inherent limitations of the visual data. In this paper, we take the depth information into consideration and construct a novel dataset of human daily actions. The proposed ACT42 dataset provides synchronized data from 4 views and 2 sources, aiming to facilitate the research of action analysis across multiple views and multiple sources. We also propose a new descriptor of depth information for action representation, which depicts the structural relations of spatiotemporal points within action volume using the distance information in depth data. In experimental validation, our descriptor obtains superior performance to the state-of-the-art action descriptors designed for color information, and more robust to viewpoint variations. The fusion of features from different sources is also discussed, and a simple but efficient method is presented to provide a baseline performance on the proposed dataset.
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Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: ICCV 2005, vol. 2, pp. 1395–1402. IEEE (2005)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: ICPR 2004, vol. 3, pp. 32–36. IEEE (2004)
Laptev, I.: On space-time interest points. IJCV 64(2), 107–123 (2005)
Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. IJCV 79(3), 299–318 (2008)
Weinland, D., Boyer, E., Ronfard, R.: Action recognition from arbitrary views using 3d exemplars. In: ICCV 2007, pp. 1–7. IEEE (2007)
Messing, R., Pal, C., Kautz, H.: Activity recognition using the velocity histories of tracked keypoints. In: ICCV 2009, pp. 104–111. IEEE (2009)
Singh, S., Velastin, S., Ragheb, H.: Muhavi: A multicamera human action video dataset for the evaluation of action recognition methods. In: International Conference on Advanced Video and Signal Based Surveillance, pp. 48–55. IEEE (2010)
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: CVPRW, pp. 9–14. IEEE (2010)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from rgbd images. In: AAAI Workshop on PAIR (2011)
Ni, B., Wang, G., Moulin, P.: Rgbd-hudaact: A color-depth video database for human daily activity recognition. In: IEEE Workshop on Consumer Depth Cameras for Computer Vision in conjunction with ICCV (2011)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: CVPR, vol. 2, p. 3 (2011)
Zhang, H., Parker, L.: 4-dimensional local spatio-temporal features for human activity recognition. In: IROS, pp. 2044–2049. IEEE (2011)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)
Wang, H., Ullah, M.M., Klaser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. In: BMVC 2009 (2009)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: CVPR 2006, vol. 2, pp. 2169–2178. IEEE (2006)
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Cheng, Z., Qin, L., Ye, Y., Huang, Q., Tian, Q. (2012). Human Daily Action Analysis with Multi-view and Color-Depth Data. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33868-7_6
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