Pedestrian detection combining RGB and dense LIDAR data | IEEE Conference Publication | IEEE Xplore

Pedestrian detection combining RGB and dense LIDAR data


Abstract:

Why is pedestrian detection still very challenging in realistic scenes? How much would a successful solution to monocular depth inference aid pedestrian detection? In ord...Show More

Abstract:

Why is pedestrian detection still very challenging in realistic scenes? How much would a successful solution to monocular depth inference aid pedestrian detection? In order to answer these questions we trained a state-of-the-art deformable parts detector using different configurations of optical images and their associated 3D point clouds, in conjunction and independently, leveraging upon the recently released KITTI dataset. We propose novel strategies for depth upsampling and contextual fusion that together lead to detection performance which exceeds that of the RGB-only systems. Our results suggest depth cues as a very promising mid-level target for future pedestrian detection approaches.
Date of Conference: 14-18 September 2014
Date Added to IEEE Xplore: 06 November 2014
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Conference Location: Chicago, IL, USA

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