DR-SPAAM: A Spatial-Attention and Auto-regressive Model for Person Detection in 2D Range Data

Dan Jia, Alexander Hermans, Bastian Leibe

Abstract:

Detecting persons using a 2D LiDAR is a challenging task due to the low information content of 2D range
data. To alleviate the problem caused by the sparsity of the
LiDAR points, current state-of-the-art methods fuse multiple
previous scans and perform detection using the combined scans.
The downside of such a backward looking fusion is that all the
scans need to be aligned explicitly, and the necessary alignment
operation makes the whole pipeline more expensive – often too
expensive for real-world applications. In this paper, we propose
a person detection network which uses an alternative strategy
to combine scans obtained at different times. Our method,
Distance Robust SPatial Attention and Auto-regressive Model
(DR-SPAAM), follows a forward looking paradigm. It keeps
the intermediate features from the backbone network as a
template and recurrently updates the template when a new
scan becomes available. The updated feature template is in
turn used for detecting persons currently in the scene. On the
DROW dataset, our method outperforms the existing state-ofthe-art, while being approximately four times faster, running at
87.2 FPS on a laptop with a dedicated GPU and at 22.6 FPS on
an NVIDIA Jetson AGX embedded GPU. We release our code
in PyTorch and a ROS node including pre-trained models.