Tell the truth from the front: anti-disguise vehicle re-identification

Recent efforts have been increasingly made on vehicle re-identification (re-ID), which has huge contributions to intelligent transportation and criminal investigation. However, most existing methods heavily rely on the color and texture features of vehicles to discern their identities, which turn invalid under adversarial social security occasions where vehicles' color and style are always tampered or forged by crime suspects. In this paper, we propose a local feature preservation method to learn the structure-aware features from the position distribution of individual local regions within vehicle front window area, which appear more robust and discriminative upon disguise. We further develop a two-branch deep convolutional network framework to integrate the structure-aware features with vehicle model features for vehicle Re-ID. The experimental results on datasets VehicleID and Vehicle-1M show that our end-to-end framework achieves promising performance and outperforms the state-of-the-art methods proposed so far.