Abstract
Robust solutions to vision-based human action recognition require effective representations of body shapes and their dynamics. Combining multiple cues in the input space can improve the recognition task. Although conventional method such as concatenation of feature vectors is straightforward, it may not sufficiently encapsulate the characteristics of an action. Inspired by the success of convolution-based reverb application in digital signal processing, we propose a novel method to synergistically combine shape and motion histograms via convolution operation. The objective is to synthesize the output (action representation) which carries the characteristics of both source inputs (shape and motion). Analysis and experimental results on the Weizmann and KTH datasets show that the resultant feature is more efficient than other hybrid features. Compared to other recent works, the feature that we used has much lower dimension. In addition, our method avoids the need for determining weights manually during feature concatenation.
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References
Laptev, I., Lindeberg, T.: Space-time interest points. In: ICCV, Nice, France, pp. 432–439 (2003)
Dollár, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS, Beijing, China, pp. 65–72 (2005)
Willems, G., Tuytelaars, T., Van Gool, L.: An efficient dense and scale-invariant spatio-temporal interest point detector. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 650–663. Springer, Heidelberg (2008)
Efros, A.A., Berg, A.C., Mori, G., Malik, J.: Recognizing action at a distance. In: ICCV, Nice, France, pp. 726–733 (2003)
Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. In: ICCV, Beijing, China, pp. 1395–1402 (2005)
Ikizler, N., Cinbis, R.G., Duygulu, P.: Human action recognition with line and flow histograms. In: ICPR, Tampa, FL, pp. 1–4 (2008)
Ikizler, N., Duygulu, P.: Histogram of oriented rectangles: A new pose descriptor for human action recognition. Image and Vision Computing 27, 1515–1526 (2009)
Lin, Z., Jiang, Z., Davis, L.S.: Recognizing actions by shape-motion prototype trees. In: ICCV, Kyoto, Japan (2009)
Schindler, K., Van Gool, L.: Action snippets: How many frames does human action recognition require? In: CVPR, Anchorage, Alaska, pp. 1–8 (2008)
Jhuang, H., Serre, T., Wolf, L., Poggio, T.: A biologically inspired system for action recognition. In: ICCV, Rio de Janeiro, Brazil, pp. 1–8 (2007)
Chaudhry, R., Ravichandran, A., Hager, G., Vidal, R.: Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions. In: CVPR, Miami, FL, USA, pp. 1932–1939 (2009)
Poppe, R.: A survey on vision-based human action recognition. Image and Vision Computing 28, 976–990 (2010)
Wang, H., Ullah, M.M., Kläser, A., Laptev, I., Schmid, C.: Evaluation of local spatio-temporal features for action recognition. British Machine Vision Conference (BMVC), 127 (2009)
Niebles, J.C., Wang, H., Fei-fei, L.: Unsupervised learning of human action categories using spatial-temporal words. Int’l J. Computer Vision 79, 299–318 (2008)
Schüldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local SVM approach. In: ICPR, Cambridge, United Kingdom, pp. 32–36 (2004)
Fathi, A., Mori, G.: Action recognition by learning mid-level motion features. In: CVPR, Anchorage, Alaska, pp. 1–8 (2008)
Wang, L., Suter, D.: Recognizing human activities from silhouettes: Motion subspace and factorial discriminative graphical model. In: CVPR, Minnesota, USA, pp. 1–8 (2007)
Ahmad, M., Lee, S.: Human action recognition using shape and CLG-motion flow from multi-view image sequences. Pattern Recognition 41, 2237–2252 (2008)
Ke, Y., Sukthankar, R., Hebert, M.: Spatio-temporal shape and flow correlation for action recognition. In: 7th Int. Workshop on Visual Surveillance (2007)
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Chua, T.W., Leman, K. (2014). A Novel Human Action Representation via Convolution of Shape-Motion Histograms. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds) MultiMedia Modeling. MMM 2014. Lecture Notes in Computer Science, vol 8325. Springer, Cham. https://doi.org/10.1007/978-3-319-04114-8_9
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DOI: https://doi.org/10.1007/978-3-319-04114-8_9
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