Zero Shot License Plate Re-Identification

Abstract

The problem of person, vehicle or license plate reidentification is generally treated as a multi-shot image retrieval problem. The objective of these tasks is to learn a feature representation of query images (called a "signature") and then use these signatures to match against a database of template image signatures with the aid of a distance metric. In this paper, we propose a novel approach for license plate Re-Id inspired by Zero Shot Learning. The core idea is to generate template signatures for retrieval purposes from a multi-hot text encoding of license plates instead of their images. The proposed method maps license plate images and their license plate numbers to a common embedding space using a Symmetric Triplet loss function so that an image can be queried against its text. In effect, our approach makes it possible to identify license plates whose images have never been seen before, using a large text database of license plate numbers. We show that our system is capable of highly accurate and fast re-identification of license plates, and its performance compares favorably to both OCR-based approaches as well as state of the art image-based Re-ID approaches. In addition to the advantages of avoiding manual image labeling and the ease of creating signature databases, the minimal time and storage requirements enable our system to be deployed even on portable devices.