Event detection based on a pedestrian interaction graph using hidden Markov models

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6952 LNCS, Issue , 2011, Pages 271-283

  Burkert, Florian ^a   Butenuth, Matthias ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

aerial image sequences; Event detection; Hidden Markov Model; pedestrians; trajectory interpretation

Indexed keywords

AERIAL IMAGE SEQUENCES; CLUTTERED SCENES; EVENT DETECTION; GRAPH-BASED; IMAGE SEQUENCE; INTERACTION GRAPHS; LARGE GROUPS; MARKOV MODEL; PEDESTRIANS; TEMPORAL BEHAVIOUR;

IMAGE ANALYSIS; PHOTOGRAMMETRY;

HIDDEN MARKOV MODELS;

EID: 80054019360     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24393-6_23     Document Type: Conference Paper

References (15)

1. Adam, A., Rivlin, E., Shimshoni, I., Reinitz, D.: Robust Real-Time Unusual Event Detection using Multiple Fixed-Location Monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(3), 555-560 (2008)
2. Andrade, E.L., Blunsden, S., Fisher, R.B.: Hidden Markov Models for Optical Flow Analysis in Crowds. In: 18th International Conference of Pattern Recognition, ICPR 2006, pp. 460-463 (2006)
3. Basharat, A., Gritai, A., Shah, M.: Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection. In: 21th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008, pp. 1-8 (2008)
4. Burkert, F., Schmidt, F., Butenuth, M., Hinz, S.: People Tracking and Trajectory Interpretation in Aerial Image Sequences. In: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Comm. III (Part A), pp. 209-214 (2010)
5. Calderara, S., Cucchiara, R.: People Trajectory Mining with Statistical Pattern Recognition. In: 23rd IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2010 (2010)
6. Helbing, D., Molnar, P.: Social Force Model for Pedestrian Dynamics. Physical Review E 51(5), 4282-4286 (1995)
7. Hongeng, S., Nevatia, R., Bremond, F.: Video-Based Event Recognition: Activity Representation and Probabilistic Recognition Methods. Computer Vision and Image Understanding 96(2), 129-162 (2004)
8. Hu, W., Xiao, X., Fu, Z., Xie, D., Tan, T., Maybank, S.: A system for learning statistical motion patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(9), 1450-1464 (2006)
9. Kuettel, D., Breitenstein, M.D., Van Gool, L., Ferrari, V.: Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection. In: 23rd IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, pp. 1951-1958 (2010)
10. Mehran, R., Oyama, A., Shah, M.: Abnormal Crowd Behaviour using Social Force Model. In: 22nd IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 935-942 (2009)
11. Nascimento, J.C., Figueiredo, M.A.T., Marques, J.S.: Trajectory Classification using Switched Dynamical Hidden Markov Models. IEEE Transactions on Image Processing 19(5), 1338-1348 (2010)
12. Oliver, N.M., Rosario, B., Pentland, A.P.: A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 831-843 (2000)
13. Porikli, F., Haga, T.: Event Detection by Eigenvector Decomposition using Object and Frame Features. In: 17th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR 2004 (2004)
14. Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77(2), 257-286 (1989)
15. Zhan, B., Monekesso, D.N., Remagnino, P., Velastin, S.A., Xu, L.: Crowd Analysis: A Survey. Machine Vision and Applications 19(5-6), 345-357 (2008)