Cascaded Hierarchical Context-Aware Vehicle Re-Identification

Abstract

Vehicle Re-Identification (Re-ID) is a challenging task, which aims to match the surveillance images containing the same vehicle. Since vehicles of the same type tend to share very similar appearance, slight difference in local areas are usually critical in the vehicle Re-ID task. Recently, some fine-grained Re-ID algorithms have achieved superior performance by modeling the key areas with specific semantics such as windows, lights, car orientation, etc. However, such methods are labor-intensive to label the key areas for object detection. This work proposes a Cascaded Hierarchical Context-Aware scheme namely CHCA, which is free of fine-grained labeling, to adaptively extract the visual features of discriminative local areas based on surrounding hierarchical context information with a specially designed recursive cross-level attention mechanism. It does not require any additional supervision and is easy to be embedded in existing networks. Extensive experiments on three popular vehicle Re-ID benchmarks demonstrate the effectiveness of CHCA, which has competitive results with existing state-of-the-art methods applying fine-grained labels.