Object flow: A descriptor for classifying traffic motion

We present and evaluate a novel scene descriptor for classifying urban traffic by object motion. Atomic 3D flow vectors are extracted and compensated for the vehicle's egomotion, using stereo video sequences. Votes cast by each flow vector are accumulated in a bird's eye view histogram gri...

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Bibliographic Details
Published in2010 IEEE Intelligent Vehicles Symposium pp. 287 - 293
Main Authors Geiger, Andreas, Kitt, Bernd
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Cameras
Control systems
Intelligent vehicles
Layout
Motion control
Motion measurement
Roads
Shape
USA Councils
Video sequences
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Summary:We present and evaluate a novel scene descriptor for classifying urban traffic by object motion. Atomic 3D flow vectors are extracted and compensated for the vehicle's egomotion, using stereo video sequences. Votes cast by each flow vector are accumulated in a bird's eye view histogram grid. Since we are directly using low-level object flow, no prior object detection or tracking is needed. We demonstrate the effectiveness of the proposed descriptor by comparing it to two simpler baselines on the task of classifying more than 100 challenging video sequences into intersection and non-intersection scenarios. Our experiments reveal good classification performance in busy traffic situations, making our method a valuable complement to traditional approaches based on lane markings.
ISBN:1424478669
9781424478668
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2010.5548122