Year: 2019
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Zamprogno, Marco, Passon, Marco, Martinel, Niki, Serra, Giuseppe, Lancioni, Giuseppe, Micheloni, Christian, Tasso, Carlo, Foresti, Gian Luca
Institution: University of Udine
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2980316540