Introducing A Framework for Single-Human Tracking Using Event-Based Cameras
Abstract
Event cameras generate data based on the amount of motion present
in the captured scene, making them attractive sensors for solving ob-
ject tracking tasks. In this paper, we present a framework for tracking
humans using a single event camera which consists of three compo-
nents. First, we train a Graph Neural Network (GNN) to recognize a
person within the stream of events. Batches of events are represented
as spatio-temporal graphs in order to preserve the sparse nature of
events and retain their high temporal resolution. Subsequently, the
person is localized in a weakly-supervised manner by adopting the
well established method of Class Activation Maps (CAM) for our
graph-based classification model. Our approach does not require the
ground truth position of humans during training. Finally, a Kalman
filter is deployed for tracking, which uses the predicted bounding
box surrounding the human as measurement. We demonstrate that
our approach achieves robust tracking results on test sequences from
the Gait3 database, paving the way for further privacy-preserving
methods in event-based human tracking. Code, pre-trained models
and datasets of our research are publicly available
Origin | Files produced by the author(s) |
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