Social Groups Detection in Crowd by Using Automatic Fuzzy Clustering with PSOResearch Areas : Machine learning
Hassan Farsi 2
Sajad Mohammadzadeh 3
Keywords: Author Guide, , Article, , Camera-Ready Format, , Paper Specifications, , Paper Submission, ,
Detecting social groups is one of the most important and complex problems which has been concerned recently. This process and relation between members in the groups are necessary for human-like robots shortly. Moving in a group means to be a subsystem in the group. In other words, a group containing two or more persons can be considered to be in the same direction of movement with the same speed of movement. All datasets contain some information about trajectories and labels of the members. The aim is to detect social groups containing two or more persons or detecting the individual motion of a person. For detecting social groups in the proposed method, automatic fuzzy clustering with Particle Swarm Optimization (PSO) is used. The automatic fuzzy clustering with the PSO introduced in the proposed method does not need to know the number of groups. At first, the locations of all people in frequent frames are detected and the average of locations is given to automatic fuzzy clustering with the PSO. The proposed method provides reliable results in valid datasets. The proposed method is compared with a method that provides better results while needs training data for the training step, but the proposed method does not require training at all. This characteristic of the proposed method increases the ability of its implementation for robots. The indexing results show that the proposed method can automatically find social groups without accessing the number of groups and requiring training data at all.
 Mehran, R., Oyama, A., Shah, M., : 'Abnormal crowd behavior detection using social force model,' in Computer Vision and Pattern Recognition, Conference on IEEE., 2009, pp. 935-942.
 Manzi, A., Fiorini, L., Limosani, R., Dario, P., Cavallo, F.: 'Two-person activity recognition using skeleton data', IET Computer Vision, vol. 12, no.1, pp. 27-35, 2017.
 Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., Theraulaz G., :'The walking behaviour of pedestrian social groups and its impact on crowd dynamics,' PloS one, vol.5, no.4, pp. 10047-10054, 2010.
 Bandini, S., Gorrini, A., Manenti, L., Vizzari G.,: 'Crowd and pedestrian dynamics: Empirical investigation and simulation,' in Proceedings of Measuring Behavior, pp. 308-311, 2012.
 Van Stekelenburg, J., & Klandermans, B. Individuals in movements: A social psychology of contention. In Handbook of social movements across disciplines (pp. 103-139). Springer, Cham, 2017.
 Luber, M., Stork, J. A., Tipaldi, G. D. Arras, K. O.: 'People tracking with human motion predictions from social forces,' in Robotics and Automation (ICRA), 2010 IEEE International Conference on IEEE, pp. 464-469, 2010.
 Iqbal, T., Moosaei, M., Riek, L. D.,: 'Tempo adaptation and anticipation methods for human-robot teams,' in RSS, Planning HRI: Shared Autonomy Collab. Robot. Workshop, 2016.
 Iqbal, T., Rack, S., Riek, L. D.,: 'Movement coordination in human–robot teams: a dynamical systems approach,' IEEE Transactions on Robotics, vol.32, no.4, pp. 909-919, 2016.
 Vázquez, M., Steinfeld, A., Hudson, S. E.,: 'Parallel detection of conversational groups of free-standing people and tracking of their lower-body orientation,' in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on IEEE, pp. 3010-3017, 2015.
 Ricci, E., Varadarajan, J., Subramanian, R., Rota Bulo, S., Ahuja, N., Lanz, O.,: 'Uncovering interactions and interactors: Joint estimation of head, body orientation and f-formations from surveillance videos,' in Proceedings of the IEEE International Conference on Computer Vision, pp. 4660-4668, 2015.
 Cristani M., et al.,: 'Social interaction discovery by statistical analysis of F-formations,' in BMVC, pp 4-11, 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.
 Pang, S. K., Li, J., Godsill, S. J.,: 'Detection and tracking of coordinated groups,' IEEE Transactions on Aerospace and Electronic Systems, vol. 47, no.1, pp. 472-502, 2011.
 Rodriguez, M., Laptev, I., Sivic, J., Audibert, J.-Y.,: 'Density-aware person detection and tracking in crowds,' in Computer Vision (ICCV), 2011 IEEE International Conference on IEEE, pp. 2423-2430, 2011.
 Yamaguchi, K., Berg, A. C., Ortiz, L. E., Berg, T. L.,: 'Who are you with and where are you going?,' in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on IEEE, pp. 1345-1352, 2011.
 Chang, M.-C., Krahnstoever, N., Ge, W.,: 'Probabilistic group-level motion analysis and scenario recognition,' in Computer Vision (ICCV), 2011 IEEE International Conference on IEEE, pp. 747-754, 2011.
 Zanotto, M., Bazzani, L., Cristani, M., Murino, V.,: 'Online bayesian nonparametrics for group detection,' in Proc. of BMVC, 2012.
 Ge, W., Collins, R. T., Ruback, R. B.,: 'Vision-based analysis of small groups in pedestrian crowds,' IEEE transactions on pattern analysis and machine intelligence, vol.34, no.5, pp. 1003-1016, 2012.
 Park H. S., Shi, J.,: 'Social saliency prediction,' in Computer Vision and Pattern Recognition (CVPR), Conference on IEEE, pp. 4777-4785, 2015.
 Tran, K. N., Bedagkar-Gala, A., Kakadiaris, I. A., Shah, S. K.,: 'Social Cues in Group Formation and Local Interactions for Collective Activity Analysis,' in VISAPP, vol. 1, pp. 539-548, 2013.
 Shao, J., Dong, N., Zhao, Q.,: 'An adaptive clustering approach for group detection in the crowd,' in Systems, Signals and Image Processing (IWSSIP), 2015 International Conference on IEEE, pp. 77-80, 2015.
 Voon, W. P., Mustapha, N., Affendey, L. S., Khalid, F.,: 'A new clustering approach for group detection in scene-independent dense crowds,' in Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on IEEE, pp. 414-417, 2016.
 Khan, S. D., Vizzari, G., Bandini, S., Basalamah, S.,: 'Detection of social groups in pedestrian crowds using computer vision,' in International Conference on Advanced Concepts for Intelligent Vision Systems, Springer, pp. 249-260, 2015.
 Yight, A., Temizel, A.,: 'Individual and group tracking with the evaluation of social interactions,' IET Computer Vision, vol. 11, no.3, pp. 255-263, 2016.
 Solera, F., Calderara, S., Cucchiara, R.,: 'Social constrained structural learning for groups detection in crowd,' IEEE transactions on pattern analysis and machine intelligence, vol.38, no.5, pp. 995-1008, 2016.
 Fernando, T., Denman, S., Sridharan, S., & Fookes, C., 'GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds,' In Asian Conference on Computer Vision, Springer, Cham, pp. 314-330, 2018.
 Reicher, S. D., Spears, R., Postmes, T.,: 'A social identity model of deindividuation phenomena,' European review of social psychology, vol.6, no.1, pp. 161-198, 1995.
 Eberhart R., Kennedy, J.: 'A new optimizer using particle swarm theory,' in Micro Machine and Human Science, IEEE, pp. 39-43, 1995.
 Peng, Hong, et al. "An automatic clustering algorithm inspired by membrane computing." Pattern Recognition Letters 68 pp. 34-40, 2015.
 Farsi, H., Mozaffarian MA., Rahmani, H.,: 'Improving voice activity detection used in ITU-T G.729.B,' Proc. of 3rd WSEAS International Conference on Circuits, Systems, and Telecommunications, pp. 11-15, 2009.
 Farsi, H.,: 'Improvement of minimum traching in minimum statistics noise estimation method,' Signal Processing An International Journal (SPIJ), vol. 4, no.1, pp. 1-17, 2010.
 Khosravi, H., Moradi, E., Darabi, H.,: 'Identification Of Homogeneous Groundwater Quality Regions Using Factor And Cluster Analysis; A Case Study Ghir Plain Of Fars Province,' 2015.
 Hosseini, S. M., Farsi, H., Yazdi, H. S.,: 'Best clustering around the color images,' International Journal of Computer and Electrical Engineering, vol.1, no.1, pp. 20-29, 2009.
 Hosseini, S. M., Nasrabadi, A., Nouri, P., Farsi, H.,: 'A novel human gait recognition system,' International Journal of Computer and Electrical Engineering, vol.2, no.6, pp. 1043-1055, 2010.
 Sadeghian, A., Kosaraju, V., Sadeghian, A., Hirose, N., Rezatofighi, H., & Savarese, S. Sophie,: 'An attentive gan for predicting paths compliant to social and physical constraints,' In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1349-1358, 2019.
 Wang, Q., Chen, M., Nie, F., & Li, X., 'Detecting coherent groups in crowd scenes by multiview clustering,' IEEE transactions on pattern analysis and machine intelligence, vol.40, no.7, pp. 910-919, 2018.