Stronger Baseline for Vehicle Re-Identification in the Wild

Chih Chung Hsu, Cing Hao Hung, Chih Yu Jian, Yi Xiu Zhuang

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

Recently, re-identification tasks in computer vision field draw attention. Vehicle re-identification can be used to find the suspect car (target) from a vast surveillance video dataset. One of the most critical issues in the vehicle re-identification task is how to learn the effective feature representation. In general, pairwise learning such as the contrastive and triplet loss functions is adopted to learn the discriminative feature based on the convolution neural network. A good backbone network will lead to a significant improvement in the car re-identification task. In this paper, a stronger baseline method is proposed to achieve a better feature representation ability. First, we integrate the shift-invariant convolutional neural network with ResNet backbone to enhance the consistency feature learning. Afterward, a multi-layer feature fusion module is proposed to incorporate the middle-and high-level features to further improve the performance of car re-identification. Experimental results demonstrated that the proposed stronger baseline method achieves state-of-The-Art performance in terms of mean averaging precision.

原文English
主出版物標題2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728137230
DOIs
出版狀態Published - 2019 12月
事件34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019 - Sydney, Australia
持續時間: 2019 12月 12019 12月 4

出版系列

名字2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019

Conference

Conference34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
國家/地區Australia
城市Sydney
期間19-12-0119-12-04

All Science Journal Classification (ASJC) codes

  • 電腦視覺和模式識別
  • 訊號處理
  • 電腦網路與通信
  • 媒體技術

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