Resolving Temporal Misalignment in the Gait Recognition domain

Description

We propose a novel end-to-end approach of combining model-based and appearance-based methods to circumvent the temporal misalignment problem in the gait recognition domain. Specifically, we locate and incorporate appearance-based features into a graph-based model. Features are extracted from the GREW dataset, which consists of real world gait data. Our approach overcomes the temporal misalignment problem, a distinction from existing works that alleviate but not completely circumvent the issue.

Period	25 Oct 2023
Event title	OAGM Workshop 2023 | Patterns in One Health
Event type	Conference
Location	AustriaShow on map