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The focus of this work is detecting pedestrians, captured in a surveillance setting, and locating them in world coordinates. Commonly adopted search strategies operate in the image plane to address the object detection problem with machine learning, for example using scale-space pyramid with the sliding windows methodology or object proposals. In contrast, here a new search space is presented, which exploits camera calibration information and geometric priors. The proposed search strategy will facilitate detectors to directly estimate pedestrian presence in world coordinates of interest. Results are demonstrated on real world outdoor collected data along a path in dim light conditions, with the goal to locate pedestrians in world coordinates. The proposed search strategy indicate a mean error under 20 cm, while image plane search methods, with additional processing adopted for localization, yielded around or above 30 cm in mean localization error. This while only observing 3-4% of patches required by the image plane searches at the same task. (Less)

A Search Space Strategy for Pedestrian Detection and Localization in World Coordinates