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arXiv · 1910.04856

Video-Based Convolutional Attention for Person Re-Identification

Marco Zamprogno·Marco Passon·Niki Martinel·Giuseppe Serra·Giuseppe Lancioni·Christian Micheloni·Carlo Tasso·Gian Luca Foresti
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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.

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Marco Zamprogno, Marco Passon, Niki Martinel, Giuseppe Serra, Giuseppe Lancioni, Christian Micheloni, Carlo Tasso, Gian Luca Foresti. 2019-09-26. Video-Based Convolutional Attention for Person Re-Identification. https://arxiv.org/abs/1910.04856

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