Vehicle occlusion model for traffic monitoring

This paper focuses on modelling sensor placement for traffic monitoring using vision based sensors. A significant problem with using such sensors is that vehicles can be merged in an amorphous group making detection difficult. Sensor placement has a direct impact on the efficiency of traffic monitoring. We simulate various sensor placements and measure the apparent occlusion between vehicles. Parametric distributions are utilised for modelling vehicle height, length, width and gap length when simulating traffic mixes. These models are used to predict the probability of on-axis and off-axis occlusion of vehicles as perceived from different sensor locations. These occlusion models show that poor sensor placement can have a direct impact on the detection of vehicles.

[1]  Thomas F. La Porta,et al.  Movement-assisted sensor deployment , 2004, IEEE INFOCOM 2004.

[2]  Rahul Sukthankar,et al.  Distributed localization of networked cameras , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[3]  S. Sitharama Iyengar,et al.  Sensor placement for grid coverage under imprecise detections , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[4]  Tim Tau Hsieh Using sensor networks for highway and traffic applications , 2004 .

[5]  Jianhua Xu,et al.  Robust placement of sensors in dynamic water distribution systems , 2010, Eur. J. Oper. Res..

[6]  Galen H. Sasaki,et al.  Wireless sensor placement for reliable and efficient data collection , 2003, 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.

[7]  Andreas Krause,et al.  Simultaneous placement and scheduling of sensors , 2009, 2009 International Conference on Information Processing in Sensor Networks.

[8]  Tim Tau Hsieh Using sensor networks for highway and traffic applications , 2004, IEEE Potentials.

[9]  Thia Kirubarajan,et al.  Hierarchical markov decision processes based distributed data fusion and collaborative sensor management for multitarget multisensor tracking applications , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.