Deep End-to-end 3D Person Detection from Camera and Lidar
2019 IEEE Intelligent Transportation Systems Conference (ITSC)(2019)
Abstract
We present a method for 3D person detection from camera images and lidar point clouds in automotive scenes. The method comprises a deep neural network which estimates the 3D location and extent of persons present in the scene. 3D anchor proposals are refined in two stages: a region proposal network and a subsequent detection network.For both input modalities high-level feature representations are learned from raw sensor data instead of being manually designed. To that end, we use Voxel Feature Encoders [1] to obtain point cloud features instead of widely used projection-based point cloud representations, thus allowing the network to learn to predict the location and extent of persons in an end-to-end manner.Experiments on the validation set of the KITTI 3D object detection benchmark [2] show that the proposed method outperforms state-of-the-art methods with an average precision (AP) of 47.06% on moderate difficulty.
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Key words
end-to-end 3D person detection,camera images,lidar point clouds,automotive scenes,deep neural network,3D anchor proposals,region proposal network,subsequent detection network,input modalities high-level feature representations,raw sensor data,Voxel Feature Encoders,point cloud features,projection-based point cloud representations,KITTI 3D object detection benchmark
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