Abstract
Abnormal activity detection in a video is a challenging and attractive task. In this paper, an approach using spatio-temporal feature and Laplacian sparse representation is proposed to tackle this problem. To detect the abnormal activity, we first detect interest points of a query video in the spatio-temporal domain. Then normalized combinational vectors, named HNF, are computed around the detected space-time interest points to characterize the video. After that, we utilize the Laplacian sparse representation framework and maximum pooling method to gain a more discriminative feature vector from the HNF set. Finally, the support vector machine (SVM) is adopted to classify the feature vector as normal or abnormal. Experiments on two datasets demonstrate the satisfactory performance of the proposed approach.
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References
Boiman, O., Shechtman, E., Irani, M.: In defense of nearest-neighbor based image classification. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8. IEEE (2008)
Chang, C.C., Lin, C.J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)
Gao, S., Tsang, I.W., Chia, L.T., Zhao, P.: Local features are not lonely-laplacian sparse coding for image classification. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3555–3561. IEEE (2010)
Helbing, D., Molnar, P.: Social force model for pedestrian dynamics. Phys. Rev. E 51(5), 4282 (1995)
Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)
Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B.: Learning realistic human actions from movies. In: IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, pp. 1–8. IEEE (2008)
Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in neural information processing systems, pp. 801–808 (2006)
Mehran, R., Moore, B.E., Shah, M.: A streakline representation of flow in crowded scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part III. LNCS, vol. 6313, pp. 439–452. Springer, Heidelberg (2010)
Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009. CVPR 2009, pp. 935–942. IEEE (2009)
Bermejo Nievas, E., Deniz Suarez, O., Bueno García, G., Sukthankar, R.: Violence detection in video using computer vision techniques. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011, Part II. LNCS, vol. 6855, pp. 332–339. Springer, Heidelberg (2011)
de Souza, F.D.M., Chávez, G.C., do Valle, E., Araujo, D.A., et al.: Violence detection in video using spatio-temporal features. In: 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 224–230. IEEE (2010)
Xu, L., Gong, C., Yang, J., Wu, Q., Yao, L.: Violent video detection based on mosift feature and sparse coding. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3538–3542. IEEE (2014)
Ackonwledgements
This research is partly supported by NSFC, China (No: 61273258) and 863 Plan, China (No. 2015AA042308).
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Zhao, Y., Qiao, Y., Yang, J., Kasabov, N. (2015). Abnormal Activity Detection Using Spatio-Temporal Feature and Laplacian Sparse Representation. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_49
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DOI: https://doi.org/10.1007/978-3-319-26561-2_49
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