Neural Signatures for Licence Plate Re-identification

TL;DR

This paper explores deep learning methods for vehicle license plate re-identification, comparing neural network features and hybrid Fisher vector approaches, highlighting a hybrid method's comparable performance and better generalizability.

Contribution

It introduces a hybrid Fisher vector and neural network approach for license plate re-identification, demonstrating improved generalizability and computational efficiency.

Findings

Hybrid approach performs comparably to deep features.

Hybrid method offers computational benefits.

Higher generalizability to dissimilar datasets.

Abstract

The problem of vehicle licence plate re-identification is generally considered as a one-shot image retrieval problem. The objective of this task is to learn a feature representation (called a "signature") for licence plates. Incoming licence plate images are converted to signatures and matched to a previously collected template database through a distance measure. Then, the input image is recognized as the template whose signature is "nearest" to the input signature. The template database is restricted to contain only a single signature per unique licence plate for our problem. We measure the performance of deep convolutional net-based features adapted from face recognition on this task. In addition, we also test a hybrid approach combining the Fisher vector with a neural network-based embedding called "f2nn" trained with the Triplet loss function. We find that the hybrid approach performs comparably while providing computational benefits. The signature generated by the hybrid approach also shows higher generalizability to datasets more dissimilar to the training corpus.