Vehicle re-identification using relative entropy representation and gated recurrent unit networks

Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively proce...

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Bibliographic Details
Published inProcedia computer science Vol. 171; pp. 508 - 513
Main Authors Gupta, Arpita, Chittrarasu, Abilash, Balakrishnan, Ramadoss
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2020
Subjects
Deep Learning
GRU
VAE
Vehicle Re-Identification
Vehicle Re-Identification
GRU
VAE
Deep Learning
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ISSN1877-0509
1877-0509
DOI10.1016/j.procs.2020.04.054

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Summary:Vehicle Re-Identification is a prevailing subject in the field of computer vision technologies. Major problem in vehicle re-id is re identifying the vehicle from different viewpoints with occlusion and low illumination. In this work, we have proposed a deep-learning framework which effectively processes the latent representation of the vehicle image instances. The key to our framework is GRUs, which is used to determine the relationship between the vehicle image instances over a period of time, without compromising the computational cost, which is also used to encode the data.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2020.04.054