Learning Matching Behavior Differences for Compressing Vehicle Re-identification Models

Abstract

Vehicle re-identification matching vehicles captured by different cameras has great potential in the field of public security. However, recent vehicle re-identification approaches exploit complex networks, causing large computations in their testing phases. In this paper, we propose a matching behavior difference learning (MBDL) method to compress vehicle re-identification models for saving testing computations. In order to represent the matching behavior evolution across two different layers of a deep network, a matching behavior difference (MBD) matrix is designed. Then, our MBDL method minimizes the L1 loss function among MBD matrixes from a small student network and a complex teacher network, ensuring the student network use less computations to simulate the teacher network’s matching behaviors. During the testing phase, only the small student network is utilized so that testing computations can be significantly reduced. Experiments on VeRi776 and VehicleID datasets show that MBDL outperforms many state-of-the-art approaches in terms of accuracy and testing time performance.