Abstract
In recent years, it is desired that many surveillance cameras are set up for security purpose. If the automatic detection and recognition system of crimes and accidents, we can prevent them. Researchers work actively to realize the automatic system. Cubic Higher-order Local Auto-Correlation (CHLAC) feature has the shift-invariant property which is effective for surveillance. Thus, we use it to realize action recognition without detecting the target. The recognition of a sequence x=(x 1,...,x T ) can be defined as the estimation problem of posterior probability of it. If we assume that the feature of certain time is independent of other features in the sequence, the posterior probability can be estimated by the simple production of conditional probability of each time. However, the estimation of conditional probability is not easy task. Thus, we estimate the conditional probability by non-parametric model. This approach is simple and does not require the training of model. We evaluate our method using the KTH dataset and confirm that the proposed method outperforms conventional methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kobayashi, T., Otsu, N.: Action and Simultaneous Multiple-Person Identification Using Cubic Higher Order Loeal AutoCorrelation. In: International Conference on Pattern Recognition, pp. 741–744 (2004)
Otsu, N., Kurita, T.: A new scheme for practical flexible and intelligent vision systems. In: Proc. IAPR Workshop on Computer Vision, pp. 431–435 (1988)
Niebles, J.C., Wang, H., Fei-Fei, L.: Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words. International Journal of Computer Vision 79(3), 299–318 (2008)
Dollar, P., Rabaud, V., Cottrell, G., Belongie, S.: Behavior recognition via sparse spatio-temporal features. In: Proc. IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, pp. 65–72 (2005)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions:A local svm approach. In: Proc. International Conference on Pattern Recognition, pp. 32–36 (2004)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: Proc. International Conference on Computer Vision, pp. 166–173 (2005)
Yamaguchi, O., Fukui, K., Maeda, K.: Face recognition using temporal image sequence. In: Proc. IEEE Third International Conference on Automatic Face and Gesture Recognition, pp. 318–323 (1998)
Hotta, K.: View independent face detection based on horizontal rectangular features and accuracy improvement using combination kernel of various sizes. Pattern Recognition 42(3), 437–444 (2009)
Matsushita, Y., Wada, T.: Principal Component Hashing: An Accelerated Approximate Nearest Neighbor Search. In: Wada, T., Huang, F., Lin, S. (eds.) PSIVT 2009. LNCS, vol. 5414, pp. 374–385. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mimura, Y., Hotta, K., Takahashi, H. (2009). Action Recognition Based on Non-parametric Probability Density Function Estimation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-10520-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10519-7
Online ISBN: 978-3-642-10520-3
eBook Packages: Computer ScienceComputer Science (R0)