  This paper addresses the problem of vehicle re-identification using distance
comparison of images in CNN latent spaces.
  Firstly, we study the impact of the distance metrics, comparing performances
obtained with different metrics: the minimal Euclidean distance (MED), the
minimal cosine distance (MCD), and the residue of the sparse coding
reconstruction (RSCR). These metrics are applied using features extracted from
five different CNN architectures, namely ResNet18, AlexNet, VGG16, InceptionV3
and DenseNet201. We use the specific vehicle re-identification dataset VeRi to
fine-tune these CNNs and evaluate results. In overall, independently of the CNN
used, MCD outperforms MED, commonly used in the literature. These results are
confirmed on other vehicle retrieval datasets. Secondly, we extend the
state-of-the-art image-to-track process (I2TP) to a track-to-track process
(T2TP). The three distance metrics are extended to measure distance between
tracks, enabling T2TP. We compared T2TP with I2TP using the same CNN models.
Results show that T2TP outperforms I2TP for MCD and RSCR. T2TP combining
DenseNet201 and MCD-based metrics exhibits the best performances, outperforming
the state-of-the-art I2TP-based models. Finally, experiments highlight two main
results: i) the impact of metric choice in vehicle re-identification, and ii)
T2TP improves the performances compared to I2TP, especially when coupled with
MCD-based metrics.
