Abstract
In this paper we propose an efficient & intuitive algorithm for the design of feature vector quantization using space-time interest point in video surveillance. The performance of activity recognition is generally depend upon the quantity of significant features but with proper feature quantization one can delivered the same performance with less number of features. The basic characteristics of algorithm are discussed and demonstrated by experiment. It is scalable in nature and work efficiently under varying conditions. In an experiment section, we show that our novel feature quantization approach takes less number of features in compared to standard quantization, while delivering the same performance.
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References
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Savarese, S., Delpozo, A., Niebles, J., Fei-Fei, L.: Spatial-temporal correlatons for unsupervised action classification. In: Proc. IEEE Workshop on Motion and Video Computing, pp. 1–8 (2008)
Zarka, N., Alhalah, Z., Deeb, R.: Real-Time Human Motion Detection and Tracking. In: 3rd International Conference on ICTTA, vol. 08, pp. 1–6 (2008)
Turaga, P., Chellappa, R., Subrahmaniam, V.S., Udrea, O.: Machine Recognition of Human Activities: A Survey. IEEE Transactions on Circuits and Systems for Video Technology 18 (11), 1473–1488 (2008)
Benezeth, Y., Jodoin, P.M., Emile, B., Laurent, H., Rosenberger, C.: Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms. In: 19th IEEE International Conference on ICPR, pp. 1–4 (2008)
Liu, J., Yang, J., Zhang, Y.: Action Recognition by Multiple Features and Hyper-sphere Multi-class SVM. In: 20th IEEE International Conference on ICPR, pp. 3744–3747 (2010)
Bregonzio, M., Gong, S., Xiang, T.: Recognising Action as Clouds of Space-Time Interest Points. In: IEEE International Conference on CVPR, pp. 1948–1955 (2009)
Liu, J., Shah, M.: Learning human actions via information maximization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Blank, M., Gorelick, L., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes. In: 10th IEEE International Conference on ICCV, vol. 02, pp. 1395–1402 (2005)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior Recognition via Sparse Spatio-Temporal Features. In: 2nd Joint IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, vol. 02, pp. 65–72 (2005)
Shabani, A.H., Zelek, J.S., Clausi, D.A.: Human Action Recognition using Salient Opponent-Based Motion Features. In: 7th Canadian Conference on Computer and Robot Vision, pp. 362–369. IEEE Computer Society (2010)
Kovashka, A., Grauman, K.: Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2046–2053 (2010)
Yan, X., Luo, Y.: Making Full Use of Spatial-Temporal Interest Points: An Adaboost Aproach for Action Recognition. In: 17th IEEE International Conference on Image Processing, pp. 4677–4680 (2010)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: a local SVM approach (2004), http://www.nada.kth.se/cvap/actions/
Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as Space-Time Shapes (2005), http://www.wisdom.weizmann.ac.il/vision/SpaceTimeActions.html
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Vishwakarma, S., Agrawal, A. (2012). Representing Feature Quantization Approach Using Spatial-Temporal Relation for Action Recognition. In: Kundu, M.K., Mitra, S., Mazumdar, D., Pal, S.K. (eds) Perception and Machine Intelligence. PerMIn 2012. Lecture Notes in Computer Science, vol 7143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27387-2_13
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DOI: https://doi.org/10.1007/978-3-642-27387-2_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27386-5
Online ISBN: 978-3-642-27387-2
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