Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 521-525, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-521-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
08 Jun 2016
LONG-TERM TRACKING OF A SPECIFIC VEHICLE USING AIRBORNE OPTICAL CAMERA SYSTEMS
F. Kurz, D. Rosenbaum, H. Runge, D. Cerra, G. Mattyus, and P. Reinartz German Aerospace Center, 82234 Wessling, Germany
Keywords: vehicle tracking, airborne camera system, macroscopic traffic model Abstract. In this paper we present two low cost, airborne sensor systems capable of long-term vehicle tracking. Based on the properties of the sensors, a method for automatic real-time, long-term tracking of individual vehicles is presented. This combines the detection and tracking of the vehicle in low frame rate image sequences and applies the lagged Cell Transmission Model (CTM) to handle longer tracking outages occurring in complex traffic situations, e.g. tunnels. The CTM model uses the traffic conditions in the proximities of the target vehicle and estimates its motion to predict the position where it reappears.

The method is validated on an airborne image sequence acquired from a helicopter. Several reference vehicles are tracked within a range of 500m in a complex urban traffic situation. An artificial tracking outage of 240m is simulated, which is handled by the CTM. For this, all the vehicles in the close proximity are automatically detected and tracked to estimate the basic density-flow relations of the CTM model. Finally, the real and simulated trajectories of the reference vehicles in the outage are compared showing good correspondence also in congested traffic situations.

Conference paper (PDF, 2091 KB)


Citation: Kurz, F., Rosenbaum, D., Runge, H., Cerra, D., Mattyus, G., and Reinartz, P.: LONG-TERM TRACKING OF A SPECIFIC VEHICLE USING AIRBORNE OPTICAL CAMERA SYSTEMS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 521-525, https://doi.org/10.5194/isprs-archives-XLI-B2-521-2016, 2016.

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