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Features for social groups detection
- 2. Features for Social Groups Detection Huan-Cheng Hsu
- 3. Social Groups Detection • Detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories Solera, Francesco, Simone Calderara, and Rita Cucchiara. "Socially Constrained Structural Learning for Groups Detection in Crowd." (2015).
- 5. Identification of social groups • Two pedestrians are considered similar if their corresponding motion parameter values satisfy the following criteria -Distance between pedestrians < 2m -Relative speed difference < 0.4m/s -Relative difference in direction of travel < 3 deg McPhail, Clark, and Ronald T. Wohlstein. "Using film to analyze pedestrian behavior." Sociological Methods & Research 10.3 (1982): 347-375.
- 6. Wrong Cases
- 7. Wrong Cases
- 8. Space Issue • False negative – (15, 16)(17, 18)(73, 74) Exhibit more dilated patterns due to groups in open spaces • False positive – (36, 37)(64, 65)(66,67) Groups in crowded scenes Distance should be re-weight
- 9. Density Invariance • Groups in very crowded scenes will be more closed and compact, while groups in open spaces will tend to exhibit more dilated patterns M. Moussaid, N. Perozo, S. Garnier, D. Helbing, and G. Theraulaz, “The walking behaviour of pedestrian social groups and its impact on crowd dynamics,” PLoS ONE, vol. 5, Apr. 2010.
- 10. Clustering • Re-weight, from space to density • Cost-efficient, do not need to consider members out of groups μ μ μ σ σ σ
- 11. Framework Clustering Feature Extraction Grouping people walking close to each other with similar velocities Distance+ Degree Social-psychology theory -Distance between pedestrians < 2m -Relative speed difference < 0.4m/s -Relative difference in direction of travel < 3 deg Ge, Weina, Robert T. Collins, and R. Barry Ruback. "Vision-based analysis of small groups in pedestrian crowds." Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.5 (2012): 1003-1016.
- 12. Clustering • Divide pedestrians into K clusters – DTW+ Dominant set clustering
- 13. Degree Feature • According to my observation, "split" "together" "meet" are three meaningful features to describe interaction of two trajectories.
- 14. Degree Feature Split Meet Together Together ․Adaptive sub-sequence? ․Feature Dimension? ․Definition of Split, Meet and together?
- 15. Two Features • Not only orientation, spatial behaviors are created by people who sustain the shared interaction space between them • Use "degree" and "physical distance" to represent interaction between trajectories Zhang, Yanhao, et al. "Formation Period Matters: Towards Socially Consistent Group Detection via Dense Subgraph Seeking." Proceedings of the 5th ACM on International Conference on Multimedia Retrieval. ACM, 2015.
- 16. Experiment File Feature Precision Recall 1manko3 4 Features 72.96 % 70.52 % 2 Features 72.2 % 70.36 % 1manko29 4 Features 84 % 91.3 % 2 Features 88 % 88 % 2jiansha5 4 Features 75.47 % 76.92 % 2 Features 69.81 % 72.55 %
- 17. Thank you for your attention!