© The Institution of Engineering and Technology
A real-time scene modelling approach is presented that recognises temporary and permanent road structure change resulting from construction, accident or lane expansion and other obstructions. The system defined utilises a two-phase approach to modelling the scene. In the transitional phase, a dominant set-based graphical clustering approach is applied to understand the current scene structure from trajectory groupings, whereas the operational phase analyses the trajectories in real-time to detect anomalies such as u-turns, wrong-way or erratic drivers based on the acquired model of the scene structure and normal traffic patterns. In addition, the concept of dynamic traffic flow analysis is utilised to identify and remember temporary additions and removals of paths due to construction and accidents, as well as permanent road structure changes. An intuitive equal-arc-length sampling is applied to extract only the spatial information from the trajectory comparisons, since the spatial characteristics alone are sufficient for road structure understanding. A distance metric is developed to measure spatial difference and directional change of the path with entrance and exit awareness. Results for a publicly available dataset are provided, demonstrating that the method can efficiently model the scene, detect anomalies and capture both temporary and permanent scene reconstructions.
References
-
-
1)
-
25. Kuhn, H.W., Tucker, A.W.: ‘Nonlinear programming’. Proc. Second Berkeley Symp. Mathematical Statistics and Probability, Berkeley, CA, 1951, pp. 481–492.
-
2)
-
21. Zhou, Y., Yan, S., Huang, T.S.: ‘Detecting anomaly in videos from trajectory similarity analysis’. IEEE Int. Conf. Multimedia and Expo, 2007, pp. 1087–1090.
-
3)
-
15. Jung, C.R., Hennemann, L., Musse, S.R.: ‘Event detection using trajectory clustering and 4-D histograms’, IEEE Trans. Circuits Syst. Video Technol., 2008, 18, (11), pp. 1565–1575 (doi: 10.1109/TCSVT.2008.2005600).
-
4)
-
1. Jianming, H., Qiang, M., Qi, W., Jiajie, Z., Yi, Z.: ‘Traffic congestion identification based on image processing’, IET Intell. Transp. Syst., 2012, 6, (2), pp. 153–160 (doi: 10.1049/iet-its.2011.0124).
-
5)
-
6. Yu, X., Leong, H.W., Xu, C., Tian, Q.: ‘Trajectory-based ball detection and tracking in broadcast soccer video’, IEEE Trans. Multim., 2006, 8, (6), pp. 1164–1178 (doi: 10.1109/TMM.2006.884621).
-
6)
-
10. Saunier, N., Sayed, T.: ‘Clustering vehicle trajectories with hidden Markov models application to automated traffic safety analysis’. Joint Conf. Neural Networks, 2006, pp. 4132–4138.
-
7)
-
5. Junejo, I.N., Javed, O., Shah, M.: ‘Multi feature path modeling for video surveillance’. Proc. 17th Int. Conf. Pattern Recognition, 2004, vol. 2, pp. 716–719.
-
8)
-
2. Baskar, L.D., Schutter, B.D., Hellendoorn, J., Papp, Z.: ‘Traffic control and intelligent vehicle highway systems: a survey’, IET Intell. Transp. Syst., 2011, 5, (1), pp. 38–52 (doi: 10.1049/iet-its.2009.0001).
-
9)
-
24. Weibull, J.W.: ‘Evolutionary game theory’ (The MIT Press, 1995).
-
10)
-
B.T. Morris ,
M.M. Trivedi
.
A survey of vision-based trajectory learning and analysis for surveillance.
IEEE Trans. Circuits Syst. Video Technol.
,
8 ,
1114 -
1127
-
11)
-
X. Wang ,
X. Ma ,
W.E. Grimson
.
Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models.
IEEE Trans. Pattern Anal. Mach. Intell.
,
3 ,
539 -
555
-
12)
-
C. Piciarelli ,
C. Micheloni ,
G.L. Foresti
.
Trajectory-based anomalous event detection.
IEEE Trans. Circuits Syst. Video Technol.
,
11 ,
1544 -
1554
-
13)
-
18. Lee, J.G., Han, J., Li, X.: ‘Trajectory outlier detection: a partition-and-detect framework’. IEEE Int. Conf. Data Engineering (ICDE), 2008, pp. 140–149.
-
14)
-
23. Pavan, M., Pelillo, M.: ‘Dominant sets and pairwise clustering’, IEEE Trans. Patt. Anal. Mach. Intell., 2007, 29, pp. 167–172 (doi: 10.1109/TPAMI.2007.250608).
-
15)
-
12. Anjum, N., Cavallaro, A.: ‘Multifeature object trajectory clustering for video analysis’, IEEE Trans. Circuits Syst. Video Technol., 2008, 18, (11), pp. 1555–1564 (doi: 10.1109/TCSVT.2008.2005603).
-
16)
-
17)
-
11. Porikli, F.: ‘Trajectory pattern detection by HMM parameter space features and eigenvector clustering’. European Conf. Computer Vision, 2004.
-
18)
-
20. Jung, C.R., Jacques, J.C.S., Soldera, J., Musse, S.R.: ‘Detection of unusual motion using computer vision’. Brazilian Symp. Computer Graphics and Image Processing SIBGRAPI, 2006, pp. 349–356.
-
19)
-
19. Piciarelli, C., Foresti, G.L.: ‘On-line trajectory clustering for anomalous events detection’, Patt. Recognit. Lett., 2006, 27, (15), pp. 1835–1842 (doi: 10.1016/j.patrec.2006.02.004).
-
20)
-
7. Kim, Z.W., Malik, J.: ‘High-quality vehicle trajectory generation from video data based on vehicle detection and description’. IEEE Conf. Intelligent Transportation Systems, 2003, vol. 1, pp. 176–182.
-
21)
-
22. Li, X., Hu, W., Hu, W.: ‘A coarse-to-fine strategy for vehicle motion trajectory clustering’. Int. Conf. Pattern Recognition, Los Alamitos, CA, USA, 2006, vol. 1, pp. 591–594.
-
22)
-
9. Zhang, Z., Huang, K., Tan, T.: ‘Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes’. Int. Conf. Pattern Recognition, 2006, vol. 3, pp. 1135–1138.
-
23)
-
13. Liu, X., Lin, L., Zhu, S.C., Jin, H.: ‘Trajectory parsing by cluster sampling in spatio-temporal graph’. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2009, pp. 739–746.
-
24)
-
17. Fu, Z., Hu, W., Tan, T.: ‘Similarity-based vehicle trajectory clustering and anomaly detection’, IEEE Intern. Conf. on Image Processing, 2005, 2, pp. 602–605.
-
25)
-
3. Cheng, H.Y., Hsu, S.H.: ‘Intelligent highway traffic surveillance with self-diagnosis abilities’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (4), pp. 1462–1472 (doi: 10.1109/TITS.2011.2160171).
-
26)
-
14. Saha, B., Mitra, P.: ‘Dynamic algorithm for graph clustering using minimum cut tree’. Proc. SIAM Conf. Data Mining, 2007, pp. 581–586.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2012.0119
Related content
content/journals/10.1049/iet-its.2012.0119
pub_keyword,iet_inspecKeyword,pub_concept
6
6