Abstract
Crowd monitoring is a critical application in video surveillance. Crowd events such as running, walking, merging, splitting, dispersion, and evacuation inform crowd management about the behavior of groups of people. For an effective crowd management, detection of crowd events provides an early sign of the behavior of the people. However, crowd event detection using videos is a highly challenging task because of several challenges such as non-rigid human body motions, occlusions, unavailability of distinguishing features due to occlusions, unpredictability in people movements, and other. In addition, the video itself is a high-dimensional data and analyzing to detect events becomes further complicated. One way of tackling the huge volume of video data is to represent a video using low-dimensional equivalent. However, reducing the video data size needs to consider the complex data structure and events embedded in a video. To this extent, we focus on detection of crowd events using the Isometric Mapping (ISOMAP) and Support Vector Machine (SVM). The ISOMAP is used to construct the low-dimensional representation of the feature vectors, and then an SVM is used for training and classification. The proposed approach uses Haar wavelets to extract Gray Level Coefficient Matrix (GLCM). Later, the approach extracts four statistical features (contrast, correlating, energy, and homogeneity) at different levels of Haar wavelet decomposition. Experiment results suggest that the proposed approach is shown to perform better when compared with existing approaches.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003). doi:10.1162/089976603321780317
Benabbas, Y., Ihaddadene, N., Djeraba, C.: Motion pattern extraction and event detection for automatic visual surveillance. J. Image Video Process. 2011, 1–15 (2011). doi:10.1155/2011/163682
Chan, A.B., Morrow, M., Vasconcelos, N.: Analysis of crowded scenes using holistic properties. In: Performance Evaluation of Tracking and Surveillance workshop at CVPR, pp. 101–108. IEEE (2009)
Chang, C.C., Lin, C.J.:
Chang, Y., Hu, C., Feris, R., Turk, M.: Manifold based analysis of facial expression. Image and Vision Computing 24(6), 605–614 (2006)
Chen, Y., Zhong, Z., Ka Keung, L., Yangsheng, X.: Multi-agent based surveillance. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2810–2815. IEEE (2006). doi:10.1109/iros.2006.282064
Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: Computer Vision–ECCV 2006, pp. 428–441. Springer (2006)
Ekin, A., Mehrotra, R., et al.: Automatic soccer video analysis and summarization. IEEE Transactions on Image Processing 12(7), 796–807 (2003)
Enzweiler, M., Gavrila, D.M.: Monocular pedestrian detection: Survey and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2179–2195 (2009)
Ferryman, J.: PETS 2009 benchmark data (2009). http://www.cvg.rdg.ac.uk/PETS2009/a.html (accessed May 19, 2014)
Foresti, G.L., Micheloni, C., Snidaro, L., Remagnino, P., Ellis, T.: Active video-based surveillance system: the low-level image and video processing techniques needed for implementation. IEEE Signal Processing Magazine 22(2), 25–37 (2005)
Gárate, C., Bilinsky, P., Bremond, F.: Crowd event recognition using HOG tracker. In: 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS-Winter), pp. 1–6. IEEE (2009). doi:10.1109/pets-winter.2009.5399727
Guo, G., Fu, Y., Dyer, C.R., Huang, T.S.: Image-based human age estimation by manifold learning and locally adjusted robust regression. IEEE Transactions on Image Processing 17(7), 1178–1188 (2008)
Haar, A.: Zur theorie der orthogonalen funktionensysteme. Mathematische Annalen 69(3), 331–371 (1910)
Hotelling, H.: Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24(6), 417–441 (1933). doi:10.1037/h0071325
Hughes, R.L.: A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological 36(6), 507–535 (2002)
Ke, Y., Sukthankar, R., Hebert, M.: Volumetric features for video event detection. Interantional Journal of Computer Vision 88(3), 339–362 (2010). doi:10.1007/s11263-009-0308-z
Lee, J.M.: Riemannian Manifolds: An Introduction to Curvature, vol. 176. Springer (1997)
Li, R., Chellappa, R., Zhou, S.K.: Learning multi-modal densities on discriminative temporal interaction manifold for group activity recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2450–2457. IEEE (2009). doi:10.1109/cvpr.2009.5206676
Ma, Y., Fu, Y.: Manifold Learning Theory and Applications. CRC Press, Inc. (2011)
Mallat, S.: A wavelet tour of signal processing: the sparse way. Academic press (2008)
Pless, R., Souvenir, R.: A survey of manifold learning for images. IPSJ Transactions on Computer Vision and Applications 1, 83–94 (2009)
Rao, A.S., Gubbi, J., Marusic, S., Palaniswami, M.: Estimation of crowd density by clustering motion cues. The Visual Computer, 1–20 (2014). doi:10.1007/s00371-014-1032-4
Rao, A.S., Gubbi, J., Marusic, S., Palaniswami, M.: Probabilistic detection of crowd events on riemannian manifolds. In: 2014 International Conference on Digital lmage Computing: Techniques and Applications (DlCTA), pp. 1–8. IEEE (2014). doi:10.1109/DICTA.2014.7008124
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000). doi:10.1126/science.290.5500.2323
Souvenir, R., Pless, R.: Manifold clustering. In: Tenth IEEE International Conference on Computer Vision (ICCV), vol. 1, pp. 648–653. IEEE (2005)
Souvenir, R., Pless, R.: Image distance functions for manifold learning. Image and Vision Computing 25(3), 365–373 (2007)
Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000). doi:10.1126/science.290.5500.2319
Thida, M., How-Lung, E., Remagnino, P.: Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes. IEEE Transactions on Cybernetics 43(6), 2147–2156 (2013). doi:10.1109/TCYB.2013.2242059
Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Sixth International Conference on Computer Vision, pp. 839–846. IEEE (1998)
Torgerson, W.: Multidimensional scaling: I. theory and method. Psychometrika 17(4), 401–419 (1952). doi:10.1007/BF02288916
Utasi, Á., Kiss, Á., Szirányi, T.: Statistical filters for crowd image analysis. In: Proceedings of the 11th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (in conjunction with CVPR 2009). IEEE (2009)
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4) (2006). doi:10.1145/1177352.1177355
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Rao, A.S., Gubbi, J., Palaniswami, M. (2016). An Improved Approach to Crowd Event Detection by Reducing Data Dimensions. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-28658-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28656-3
Online ISBN: 978-3-319-28658-7
eBook Packages: EngineeringEngineering (R0)