TY - GEN
T1 - Stronger Baseline for Vehicle Re-Identification in the Wild
AU - Hsu, Chih Chung
AU - Hung, Cing Hao
AU - Jian, Chih Yu
AU - Zhuang, Yi Xiu
N1 - Funding Information:
This study was supported in part by the Ministry of Science and Technology, Taiwan, under Grants MOST 108-2634-F-007-009, 107-2218-E-020-002-MY3, and 108-2218-E-006 - 052.
Funding Information:
This study was supported in part by the Ministry of Science and Technology, Taiwan, under Grants MOST 108-2634-F-007-009, 107-2218-E-020-002-MY3, and 108-2218-E-006 -052.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079250042&partnerID=8YFLogxK
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U2 - 10.1109/VCIP47243.2019.8965867
DO - 10.1109/VCIP47243.2019.8965867
M3 - Conference contribution
AN - SCOPUS:85079250042
T3 - 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
BT - 2019 IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019
Y2 - 1 December 2019 through 4 December 2019
ER -