Mixed‐attention‐based regional soft partition network for vehicle reidentification

Abstract

Abstract The purpose of vehicle reidentification is to distinguish the same vehicle from different vehicles using different cameras. The main challenge of this task is the significant intra‐instance discrepancy of the same vehicle from different views and the subtle inter‐instance differences of similar vehicles from the same views. To address this problem, researchers have attempted to align features from different views, such as using additional metadata (colour, type, key points, mask, etc.) to improve performance. Although these attempts improve the performance of vehicle reidentification, considerable efforts are required to create additional precise annotations to the data before using these methods. This results in expensive data preparation costs, rendering these methods less convenient to use. Therefore, we propose a novel deep learning network, called a mixed‐attention‐based regional soft partition network, to address this problem. This network does not require additional metadata annotations; it only trains the identity label as a supervision signal and uses soft partition attention to identify specific vehicle regions. Experiments showed that the performance of the proposed method was comparable to that of the state‐of‐the‐art method with additional annotations on the VeRri‐776 and VERI‐Wild datasets.