Combination of Appearance and License Plate Features for Vehicle Re-Identification

In this work, we propose a two-module framework that combines appearance and corresponding license plate features for vehicle re-identification (Re-ID). In the appearance module, we design a Two-Branch Network to extract comprehensive global features. To obtain more discriminative feature representations, we propose an enhanced triplet loss (ETL) and combine ETL with softmax loss to optimize the parameter of Two-Branch Network. In the license plate module, we present a license plate Re-ID network that incorporates the bidirectional LSTMs into CNNs, which is effective for capturing the contexts in license plate images and significantly improves the performance of license plate Re-ID. We validate our method on both VeRi-776 dataset [1] and VehicleID dataset [2]. The experimental results show that our method outperforms most state-of-the-art approaches for vehicle Re-ID, even if only the appearance module is used.

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