A Dual Path Model With Adaptive Attention For Vehicle Re-Identification

Pirazh Khorramshahi, Amit Kumar, Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, Rama Chellappa

Abstract:

In recent years, attention models have been extensively
used for person and vehicle re-identification. Most reidentification methods are designed to focus attention on
key-point locations. However, depending on the orientation, the contribution of each key-point varies. In this paper,
we present a novel dual-path adaptive attention model for
vehicle re-identification (AAVER). The global appearance
path captures macroscopic vehicle features while the orientation conditioned part appearance path learns to capture
localized discriminative features by focusing attention
on the most informative key-points. Through extensive
experimentation, we show that the proposed AAVER method
is able to accurately re-identify vehicles in unconstrained
scenarios, yielding state of the art results on the challenging dataset VeRi-776. As a byproduct, the proposed system
is also able to accurately predict vehicle key-points and
shows an improvement of more than 7% over state of the
art