Attribute-guided Feature Learning Network for Vehicle Re-identification

Abstract

Vehicle re-identification (reID) plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, it poses the critical but challenging problem that is caused by various viewpoints of vehicles, diversified illuminations and complicated environments. Till now, most existing vehicle reID approaches focus on learning metrics or ensemble to derive better representation, which are only take identity labels of vehicle into consideration. However, the attributes of vehicle that contain detailed descriptions are beneficial for training reID model. Hence, this paper proposes a novel Attribute-Guided Network (AGNet), which could learn global representation with the abundant attribute features in an end-to-end manner. Specially, an attributeguided module is proposed in AGNet to generate the attribute mask which could inversely guide to select discriminative features for category classification. Besides that, in our proposed AGNet, an attribute-based label smoothing (ALS) loss is presented to better train the reID model, which can strength the distinct ability of vehicle reID model to regularize AGNet model according to the attributes. Comprehensive experimental results clearly demonstrate that our method achieves excellent performance on both VehicleID dataset and VeRi-776 dataset.

AttributeNet: Attribute Enhanced Vehicle Re-Identification

“…Some works exploit attribute information [7,6,18,13,12] or combine attributes with other clues [8,5,4]. Most of the previous attribute-based works use attribute information to regularize the feature learning [8,7,5,6,4,12,13].…”

“…Some works exploit attribute information [7,6,18,13,12] or combine attributes with other clues [8,5,4]. Most of the previous attribute-based works use attribute information to regularize the feature learning [8,7,5,6,4,12,13]. In general, they regress the attribute classes from the backbone features, along with the ReID supervision based on the backbone features.…”

“…Recently, there is a trend to explore additional clues for better V-ReID, such as using semantic maps [3], attributes (e.g., type, color) [4,5,6,7,8], viewpoints [9], and vehicle parts [9,10,11]. In this work, we focus on the exploration of attributes to enhance the discrimination power of feature representations.…”

“…Most of the previous attribute-based works [8,7,5,6,4,12,13] share a common characteristic in their design:…”

“…However, there is a lack of effective interaction between the attribute-based branches and V-ReID branch, where the attribute modules learn features for attribute recognition but are not explicitly designed to serve for V-ReID. Wang et al [6] explore attributes to generate attention masks, but these masks are used only to filter the information from the global feature instead of introducing the rich attribute representation into the final feature representation.…”