A Two Stream Siamese Convolutional Neural Network for Person Re-Identification

Dahjung Chung, Khalid Tahboub, Edward J. Delp; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1983-1991

Abstract


Person re-identification is an important task in video surveillance systems. It can be formally defined as establishing the correspondence between images of a person taken from different cameras at different times. In this pa- per, we present a two stream convolutional neural network where each stream is a Siamese network. This architecture can learn spatial and temporal information separately. We also propose a weighted two stream training objective function which combines the Siamese cost of the spatial and temporal streams with the objective of predicting a person's identity. We evaluate our proposed method on the publicly available PRID2011 and iLIDS-VID datasets and demonstrate the efficacy of our proposed method. On average, the top rank matching accuracy is 4% higher than the accuracy achieved by the cross-view quadratic discriminant analysis used in combination with the hierarchical Gaussian descriptor (GOG+XQDA), and 5% higher than the recurrent neural network method.

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[bibtex]
@InProceedings{Chung_2017_ICCV,
author = {Chung, Dahjung and Tahboub, Khalid and Delp, Edward J.},
title = {A Two Stream Siamese Convolutional Neural Network for Person Re-Identification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}