Abstract
Crowd analysis has become an important topic of research for visual surveillance community. This paper proposes an active contour-based trajectory clustering approach for crowd flow segmentation. To this end, the active contour method is applied to segment the foreground crowd region with an aim to optimize further tracking. From the segmented foreground region, spatiotemporal interest points are detected and tracked to extract crowd trajectories. The trajectories are then parameterized by their shape, location information, flow direction, and neighborhood density. A clustering algorithm is designed to cluster these trajectories, and further flow patterns are segmented by merging trajectory clusters on the basis of their spatial overlapping and distinction in location and in flow direction. Once the flow patterns are segmented, trajectory density of each segment is estimated to analyze crowd flow. Experiments are conducted on three publicly available UCF Web, Collective Motion, and Violent Flows crowd datasets. The proposed work is compared with various state-of-the-art methods and achieves remarkable accuracy while maintaining the lower computational complexity.
Similar content being viewed by others
References
Ali, S., Shah, M.: A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR’07, pp. 1–6. IEEE (2007)
Biswas, S., Babu, R.V.: Real time anomaly detection in h. 264 compressed videos. In: 2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), pp. 1–4. IEEE (2013)
Biswas, S., Praveen, R.G., Babu, R.V.: Super-pixel based crowd flow segmentation in h. 264 compressed videos. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2319–2323. IEEE (2014)
Brox, T., Malik, J.: Object segmentation by long term analysis of point trajectories. In: European Conference on Computer Vvision, pp. 282–295. Springer (2010)
Cao, L., Zhang, X., Ren, W., Huang, K.: Large scale crowd analysis based on convolutional neural network. Pattern Recognit. 48(10), 3016–3024 (2015)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)
Chen, L., Shen, J., Wang, W., Ni, B.: Video object segmentation via dense trajectories. IEEE Trans. Multimed. 17(12), 2225–2234 (2015)
Cheriyadat, A.M., Radke, R.J.: Detecting dominant motions in dense crowds. IEEE J. Sel. Top. Signal Process. 2(4), 568–581 (2008)
Dehghan, A., Kalayeh, M.M.: Understanding crowd collectivity: a meta-tracking approach. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1–9. Citeseer (2015)
Dong, X., Shen, J., Yu, D., Wang, W., Liu, J., Huang, H.: Occlusion-aware real-time object tracking. IEEE Trans. Multimed. 19(4), 763–771 (2017)
Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. Int. J. Comput. Vis. 88(2), 303–338 (2010)
Fradet, M., Robert, P., Pérez, P.: Clustering point trajectories with various life-spans. In: Conference for Visual Media Production, 2009. CVMP’09, pp. 7–14. IEEE (2009)
Kruthiventi, S. S. and Babu, R. V. Crowd flow segmentation in compressed domain using crf. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3417–3421. IEEE (2015)
Kuhn, A., Senst, T., Keller, I., Sikora, T., Theisel, H.: A Lagrangian framework for video analytics. In: 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP), pp. 387–392. IEEE (2012)
Lamba, S., Nain, N.: Multi-source approach for crowd density estimation in still images. In: 2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), pp. 1–6. IEEE (2017)
Lamba, S., Nain, N.: A texture based mani-fold approach for crowd density estimation using Gaussian Markov random field. Multimed. Tools Appl. 78(5), 1–20 (2018)
Lamba, S., Nain, N., Chahar, H.: A robust multi-model approach for face detection in crowd. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 96–103. IEEE (2016)
Lim, M.K., Kok, V.J., Loy, C.C., Chan, C.S.: Crowd saliency detection via global similarity structure. In: 2014 22nd International Conference on Pattern Recognition (ICPR), pp. 3957–3962. IEEE (2014)
Lin, W., Mi, Y., Wang, W., Wu, J., Wang, J., Mei, T.: A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes. IEEE Trans. Image Process. 25(4), 1674–1687 (2016)
Loy, C.C., Xiang, T., Gong, S.: Salient motion detection in crowded scenes. In: 2012 5th International Symposium on Communications Control and Signal Processing (ISCCSP), pp. 1–4. IEEE (2012)
Lu, W.-C., Wang, Y.-C.F., Chen, C.-S.: Learning dense optical-flow trajectory patterns for video object extraction. In: 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 315–322. IEEE (2010)
Magnenat-Thalmann, N., Thalmann, D.: Virtual humans: thirty years of research, what next? Vis. Comput. 21(12), 997–1015 (2005)
Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1975–1981. IEEE (2010)
Mao, Y., Li, Z., Li, Y., He, W.: Emotion-based diversity crowd behavior simulation in public emergency. Vis. Comput. 34, 1–15 (2018)
Musse, S.R., Ulicny, B., Aubel, A., Thalmann, D.: Groups and crowd simulation. In: ACM SIGGRAPH 2005 Courses, p. 2. ACM (2005)
Online. World population clock. https://www.census.gov/popclock/. Accessed 25 June 2018
Rao, A.S., Gubbi, J., Marusic, S., Palaniswami, M.: Estimation of crowd density by clustering motion cues. Vis. Comput. 31(11), 1533–1552 (2015)
Rodriguez, M., Sivic, J., Laptev, I., Audibert, J.-Y.: Data-driven crowd analysis in videos. In: ICCV 2011-13th International Conference on Computer Vision, pp. 1235–1242. IEEE (2011)
Shao, J., Change Loy, C., Wang, X.: Scene-independent group profiling in crowd. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2219–2226 (2014)
Shen, J., Hao, X., Liang, Z., Liu, Y., Wang, W., Shao, L.: Real-time superpixel segmentation by dbscan clustering algorithm. IEEE Trans. Image Process. 25(12), 5933–5942 (2016)
Shen, J., Peng, J., Shao, L.: Submodular trajectories for better motion segmentation in videos. IEEE Trans. Image Process. 27(6), 2688–2700 (2018a)
Shen, J., Yu, D., Deng, L., Dong, X.: Fast online tracking with detection refinement. IEEE Trans. Intell. Transp. Syst. 19(1), 162–173 (2018b)
Singh, N., Arya, R., Agrawal, R.: A novel position prior using fusion of rule of thirds and image center for salient object detection. Multimed. Tools Appl. 76(8), 10521–10538 (2017)
Sipiran, I., Bustos, B.: Harris 3d: a robust extension of the Harris operator for interest point detection on 3d meshes. Vis. Comput. 27(11), 963 (2011)
Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical Report CMU-CS-91-132, Carnegie Mellon University (1991)
Tripathi, G., Singh, K., Vishwakarma, D.K.: Convolutional neural networks for crowd behaviour analysis: a survey. Vis. Comput. 35(5), 1–24 (2018)
Wang, W., Shen, J., Porikli, F., Yang, R.: Semi-supervised video object segmentation with super-trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 985–998 (2019)
Wu, S., San Wong, H.: Crowd motion partitioning in a scattered motion field. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(5), 1443–1454 (2012)
Wu, S., Yu, Z., and Wong, H.-S.: Crowd flow segmentation using a novel region growing scheme. In: Pacific-Rim Conference on Multimedia, pp. 898–907. Springer (2009a)
Wu, S., Yu, Z., Wong, H.-S.: A shape derivative based approach for crowd flow segmentation. In: Asian Conference on Computer Vision, pp. 93–102. Springer (2009b)
Wu, Y., Wang, Y., Jia, Y.: Adaptive diffusion flow active contours for image segmentation. Comput. Vis. Image Underst. 117(10), 1421–1435 (2013)
Zhao, J., Xu, Y., Yang, X., Yan, Q.: Crowd instability analysis using velocity-field based social force model. In: Visual Communications and Image Processing (VCIP), 2011 IEEE, pp. 1–4. IEEE (2011)
Zhou, B., Tang, X., Wang, X.: Coherent filtering: detecting coherent motions from crowd clutters. In: Computer Vision—ECCV 2012, pp. 857–871. Springer (2012)
Zhou, B., Tang, X., Wang, X.: Measuring crowd collectiveness. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3049–3056 (2013)
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Sonu Lamba declares that she has no conflict of interest. Neeta Nain declares that she has no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Lamba, S., Nain, N. Segmentation of crowd flow by trajectory clustering in active contours. Vis Comput 36, 989–1000 (2020). https://doi.org/10.1007/s00371-019-01713-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00371-019-01713-7