Video-Based Convolutional Attention for Person Re-Identification
release_s7imn4i7qncxzfsfpjmjrsxovi
by
Marco Zamprogno, Marco Passon, Niki Martinel, Giuseppe Serra, Giuseppe
Lancioni, Christian Micheloni, Carlo Tasso, Gian Luca Foresti
2019
Abstract
In this paper we consider the problem of video-based person
re-identification, which is the task of associating videos of the same person
captured by different and non-overlapping cameras. We propose a Siamese
framework in which video frames of the person to re-identify and of the
candidate one are processed by two identical networks which produce a
similarity score. We introduce an attention mechanisms to capture the relevant
information both at frame level (spatial information) and at video level
(temporal information given by the importance of a specific frame within the
sequence). One of the novelties of our approach is given by a joint concurrent
processing of both frame and video levels, providing in such a way a very
simple architecture. Despite this fact, our approach achieves better
performance than the state-of-the-art on the challenging iLIDS-VID dataset.
In text/plain
format
Archived Files and Locations
application/pdf 530.9 kB
file_3gdu7t37nbdivnl6egd5im2ct4
|
arxiv.org (repository) web.archive.org (webarchive) |
1910.04856v1
access all versions, variants, and formats of this works (eg, pre-prints)