Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 9-15, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3-W2/9/2015/
doi:10.5194/isprsarchives-XL-3-W2-9-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
10 Mar 2015
AUTOMATIC VEHICLE TRAJECTORY EXTRACTION FOR TRAFFIC ANALYSIS FROM AERIAL VIDEO DATA
J. Apeltauer1, A. Babinec2, D. Herman2, and T. Apeltauer1 1Faculty of Civil Engineering, Brno University of Technology, Czech Republic
2RCE systems s.r.o., Svatopluka Čecha 1d, 612 00 Brno, Czech Republic
Keywords: traffic monitoring, vehicle detection, vehicle tracking, aerial imagery, UAV, particle filter Abstract. This paper presents a new approach to simultaneous detection and tracking of vehicles moving through an intersection in aerial images acquired by an unmanned aerial vehicle (UAV). Detailed analysis of spatial and temporal utilization of an intersection is an important step for its design evaluation and further traffic inspection. Traffic flow at intersections is typically very dynamic and requires continuous and accurate monitoring systems. Conventional traffic surveillance relies on a set of fixed cameras or other detectors, requiring a high density of the said devices in order to monitor the intersection in its entirety and to provide data in sufficient quality. Alternatively, a UAV can be converted to a very agile and responsive mobile sensing platform for data collection from such large scenes. However, manual vehicle annotation in aerial images would involve tremendous effort. In this paper, the proposed combination of vehicle detection and tracking aims to tackle the problem of automatic traffic analysis at an intersection from visual data. The presented method has been evaluated in several real-life scenarios.
Conference paper (PDF, 4340 KB)


Citation: Apeltauer, J., Babinec, A., Herman, D., and Apeltauer, T.: AUTOMATIC VEHICLE TRAJECTORY EXTRACTION FOR TRAFFIC ANALYSIS FROM AERIAL VIDEO DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W2, 9-15, doi:10.5194/isprsarchives-XL-3-W2-9-2015, 2015.

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