This paper proposes a semantic mapping method based on pedestrian activity in the urban road environment. Pedestrian activity patterns are learned from pedestrian tracks collected by a mobile platform. With the learned knowledge of pedestrian activity, semantic mapping is performed using Bayesian classification techniques. The proposed method is tested in real experiments, and shows promising results in recognizing four activity-related semantic properties of the urban road environment: pedestrian path, entrance/exit, pedestrian crossing and sidewalk.
Learning pedestrian activities for semantic mapping
2014-05-01
2089910 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Semantic Synthesis of Pedestrian Locomotion
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