Authors:
Mikael Nilsson
;
Martin Ahrnbom
;
Håkan Ardö
and
Aliaksei Laureshyn
Affiliation:
Lund University, Sweden
Keyword(s):
Pedestrian, Detection, World Coordinates, Machine Learning, Camera Calibration.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
Abstract:
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.
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