1 research outputs found
A Gaussian Process Convolution Particle Filter for Multiple Extended Objects Tracking with Non-Regular Shapes
Extended object tracking has become an integral
part of various autonomous systems in diverse fields. Although
it has been extensively studied over the past decade, many
complex challenges remain in the context of extended object
tracking. In this paper, a new method for tracking multiple
irregularly shaped extended objects using surface measurements
is proposed. The Gaussian Process Convolution Particle Filter
proposed in [1], designed to track a single extended/group object,
is enhanced for tracking multiple extended objects. A convolution
kernel is proposed to estimate the multi-object likelihood. A
target birth/death model based on the proposed method is also
introduced for automatic initiation and deletion of the objects.
The proposed approach is validated on real-world LiDAR data
which shows that the method is efficient in tracking multiple
irregularly shaped extended objects in challenging scenarios
involving occlusion, dense clutter and low object detection