Fig 5 - uploaded by Santiago Gerling Konrad
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Sequence excerpts of the scenario whose results are presented in figure 6. Red squares are the wrong pedestrian detection and green squares are the place where OpenPose draw the skeleton. Frames 5, 15, and 26 fail in the place where the skeleton should be drawn. Frames 20-25 have absence of skeletons because of the direct sunlight in the camera, meaning no data in the frames.
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An essential task to prevent pedestrian injuries by an autonomous vehicle is the ability to correctly detect and predict its movement. A deep learning-based 2D human poses detector, as OpenPose, provides a skeleton of people present in an image captured by cameras mounted in the car. Nevertheless, these kinds of algorithms give a frame solution but...
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