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A framework with a multi-task CNN model joint with a re-ranking method for vehicle re-identification

Published: 17 August 2018 Publication History

Abstract

Recently, with the number of surveillance cameras growing rapidly, vehicle re-identification (re-id) plays a major part in the traffic surveillance. Most exiting methods for vehicle re-id are mainly focused on single convolutional neural network (CNN) model i.e. identification or verification model to extract features. However, single model has their own drawbacks and it can not extract enough discriminative feature. In this work, we propose a new framework with a multi-task CNN model and a ranking optimization method to tackle the re-id task. The multi-task CNN model combines the strengths of the two models, which can get a more discriminative feature of vehicle image. The ranking optimization method utilizes the relationship of the k-nearest neighbors of the probe and the gallery image to optimize the final ranking list. Experiments are carried out to demonstrate that the good performance of our framework with improvements of 1.5% and 20% at the top-1 ranking matching accuracy on two mainstream datasets VeRi and VehicleID.

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Cited By

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  • (2021)Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive ReviewMathematics10.3390/math92431629:24(3162)Online publication date: 8-Dec-2021
  • (2019)Efficient and Deep Vehicle Re-Identification Using Multi-Level Feature ExtractionApplied Sciences10.3390/app90712919:7(1291)Online publication date: 27-Mar-2019

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  1. A framework with a multi-task CNN model joint with a re-ranking method for vehicle re-identification

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      cover image ACM Other conferences
      ICIMCS '18: Proceedings of the 10th International Conference on Internet Multimedia Computing and Service
      August 2018
      243 pages
      ISBN:9781450365208
      DOI:10.1145/3240876
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      Publication History

      Published: 17 August 2018

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      Author Tags

      1. convolutional neural network(CNN) models
      2. ranking optimization
      3. vehicle re-identification

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      ICIMCS '18 Paper Acceptance Rate 46 of 116 submissions, 40%;
      Overall Acceptance Rate 163 of 456 submissions, 36%

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      View all
      • (2021)Trends in Vehicle Re-Identification Past, Present, and Future: A Comprehensive ReviewMathematics10.3390/math92431629:24(3162)Online publication date: 8-Dec-2021
      • (2019)Efficient and Deep Vehicle Re-Identification Using Multi-Level Feature ExtractionApplied Sciences10.3390/app90712919:7(1291)Online publication date: 27-Mar-2019

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