Learning multi-scale deep features for person re-identification

Wanting Guan; Yanduo Zhang

doi:10.1117/12.2631934

14 April 2022 Learning multi-scale deep features for person re-identification

Wanting Guan, Yanduo Zhang

Proceedings Volume 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021); 121780H (2022) https://doi.org/10.1117/12.2631934
Event: International Conference on Signal Processing and Communication Technology (SPCT 2021), 2021, Tianjin, China

Abstract

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple cameras. With the advancement of deep network and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. In this paper, we propose a simple yet effective Multi-Scale Horizontal (MSH) model for person Re-ID task. Firstly, the model consists of a novel Multi-branch network which adopted residual network ResNet50. There are two branches in our network: global branch and local branch. In local branch, the model slice a person into different parts in multi-scales. Secondly, we present a mix pooling method which considering both average and maximum pooling method. Finally, we employ triple loss and softmax loss as the loss function of the network. Experiments on two datasets (Market1501 and DukeMTMC-reID) demonstrate the advantage of the proposed model.

Citation Download Citation

Wanting Guan and Yanduo Zhang "Learning multi-scale deep features for person re-identification", Proc. SPIE 12178, International Conference on Signal Processing and Communication Technology (SPCT 2021), 121780H (14 April 2022); https://doi.org/10.1117/12.2631934

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KEYWORDS

Data modeling

Network architectures

Cameras

High power microwaves

Visual process modeling

Feature extraction

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