Minimizing Vehicle Re-Identification Dataset Bias Using Effective Data Augmentation Method

Datasets are the important part of vehicle re-identification (re-id) research. The dataset which represents real world environment is crucial to vehicle re-id steps such as learning visual features, vehicle detection, examining performance of vehicle re-id algorithms, and so on. Often vehicle re-id datasets lacks in this context. In this paper, firstly, we investigate the vehicle re-id datasets bias problem using deep CNN model inception-v3 (Dataset classification). Dataset classification results indicates that current available vehicle re-id datasets are highly biased. Secondly, we present novel data augmentation technique to mitigate this issue by inserting additional type of variability in training set. Extensive experimental results shows that our approach can be helpful to minimize training set bias. Consequently, cross dataset vehicle re-id performance improves.