VehicleNet: Learning Robust Feature Representation for Vehicle Re-identification

7 Aug 2020  ·  Zhedong Zheng ; Tao Ruan ; Yunchao Wei ; Yi Yang ; Tao Mei ·

One fundamental challenge of vehicle re-identification (re-id) is to learn robust and discriminative visual representation, given the significant intra-class vehicle variations across different camera views. As the existing vehicle datasets are limited in terms of training images and viewpoints, we propose to build a unique large-scale vehicle dataset (called VehicleNet) by harnessing four public vehicle datasets, and design a novel yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet. The first stage of our approach is to learn the generic representation for all domains (i.e., source vehicle datasets) by training with the conventional classification loss. This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain. The second stage is to fine-tune the trained model purely based on the target vehicle set, by minimizing the distribution discrepancy between our VehicleNet and any target domain. We discuss our proposed multi-source dataset VehicleNet and evaluate the effectiveness of the two-stage progressive representation learning through extensive experiments. We achieve the state-of-art accuracy of 86.07% mAP on the private test set of AICity Challenge, and competitive results on two other public vehicle re-id datasets, i.e., VeRi-776 and VehicleID. We hope this new VehicleNet dataset and the learned robust representations can pave the way for vehicle re-id in the real-world environments.

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Results from the Paper

 Ranked #1 on Vehicle Re-Identification on VeRi (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Vehicle Re-Identification VehicleID Large vehiclenet Rank-1 79.46 # 7
Vehicle Re-Identification VehicleID Medium vehiclenet Rank-1 81.35 # 7
Vehicle Re-Identification VehicleID Small vehiclenet Rank-1 83.64 # 8
Vehicle Re-Identification VeRi vehiclenet mAP 83.41 # 1
Rank-1 96.78 # 1


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