Pedestrian crossing detection based on evidential fusion of video-sensors

Transportation Research Part C: Emerging Technologies

Volumn 17, Issue 5, 2009, Pages 484-497

  Boudet, Laurence ^a   Midenet, Sophie ^a  

^a UNIVERSITÉ PARIS EST   (France)

Author keywords

Multi purpose video sensor; Multi sensor fusion; Pedestrian crossing detection; Theory of evidence; Urban traffic management

Indexed keywords

ARTIFICIAL INTELLIGENCE; CROSSWALKS; FOOTBRIDGES; PATTERN RECOGNITION; TRAFFIC CONTROL; TRAFFIC SIGNALS;

MULTI-SENSOR FUSION; PEDESTRIAN CROSSING DETECTION; THEORY OF EVIDENCE; URBAN TRAFFIC MANAGEMENT; VIDEO SENSORS;

SENSOR DATA FUSION;

DATA PROCESSING; EUROPEAN UNION; PEDESTRIAN; SENSOR; TRAFFIC MANAGEMENT; URBAN TRANSPORT;

EID: 67949092774     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2009.04.009     Document Type: Article

References (18)

1. Aubert, D., Bouzar, S., Lenoir, F., Blosseville, J.M., 1996. Automic vehicle queue measurement at intersections using image-processing. In: Proceedings of the 8th International Conference on Road Traffic Monitoring and Control, 422, London, pp. 100-104.
2. Boillot F., Midenet S., and Pierrelée J.C. The real-time urban traffic control system CRONOS: algorithm and experiments. Transportation Research C 14 (2006) 18-39
3. Boudet, L., Midenet, S., 2008. A spatiotemporal data fusion model for occupancy state estimation: an evidential approach. In: Proceedings of the 11th International Conference on Information Fusion, Cologne, 30 June-3 July 2008, pp. 1333-1339.
4. Catchpole J. Win-win outcomes for pedestrians and drivers by optimizing traffic signal timing. Road and Transport Research Journal 12 3 (2003) 74-82
5. Dempster A. A generalization of bayesian inference. Journal of the Royal Statistical Society 30 (1968) 205-247
6. Dubois D., and Prade H. Representation and combination of uncertainty with belief functions and possibility measures. Computational Intelligence 4 (1988) 244-264
7. Hughes R., Huang H., Zegeer C., and Cynecki M. Automated detection of pedestrians in conjunction with standard pedestrian push button at signalized intersections. Transportation Research Record 1705 (2000) 32-39
8. McLeod, F.N., Hounsell, N.B., Rajbhandari, B., 2004. Improving traffic signal control for pedestrians. In: IEE International Conference on Road Transport Information and Control 12, London, 20-22 April 2004, pp. 268-277.
9. Midenet S., Boillot F., and Pierrelée J.C. Signalized intersection with real-time adaptive control: on-field assessment of CO2 and pollutant emission prediction. Transportation Research D 9 (2004) 29-47
10. Ramasso E., Rombaut M., and Pellerin P. State filtering and change detection using TBM conflict Application to human action recognition in athletics videos. IEEE Transactions on Circuits and Systems for Video Technology 17 7 (2007) 944-949
11. Shafer G. A Mathematical Theory of Evidence (1976), Princeton University Press
12. Smets P. Constructing the pignistic probability Function in a context of uncertainty. Uncertainty in Artificial Intelligence 5 (1990) 29-39
13. Smets P. Belief functions: the disjunctive rule of combination and the generalized Bayesian theorem. International Journal of Approximate Reasoning 9 1 (1993) 1-35
14. Smets P. Analyzing the combination of conflicting belief functions. Information Fusion 8 4 (2007) 387-412
15. Smets P., and Kennes R. The transferable belief model. Artificial Intelligence 66 (1994) 191-243
16. Smets, P., Ristic, B., 2007. Kalman filter and joint tracking and classification based on belief functions in the TBM framework. In: Information Fusion, Special Issue on the Seventh International Conference on Information Fusion, Part II, 8(1), pp. 16-27.
17. TRACKSS project, 2008. .
18. Yager R.R. On the Dempster-Shafer framework and new combination rules. Information Sciences 41 (1987) 93-137