Pedestrians are the most vulnerable participants to urban traffic. The first step toward protecting pedestrians is to reliably detect them. We present a new approach for standing and walking pedestrian detection, in urban traffic conditions, using greyscale stereo cameras mounted on board a vehicle.
Our system uses pattern matching and motion for pedestrian detection. Both 2–D image intensity information and 3–D dense stereo information are used for classification. The 3–D data is used for effective pedestrian hypothesis generation, scale and depth estimation and 2–D model selection.
The scaled models are matched against the selected hypothesis using a high performance, elastic matching, based on the Chamfer distance. Kalman filtering is used to track detected pedestrians. A subsequent validation, based on the motion field’s variance and periodicity of tracked, walking pedestrians is used to eliminate false positives.
Source: University of Coimbra
Author: Sergiu Nedevschi | Corneliu Tomiuc | Silviu Bota
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