Multi-Attribute Enhancement Network for Person Search
This work addresses person search for surveillance and security applications by improving retrieval accuracy through attribute-based features, representing an incremental advancement over existing methods.
The paper tackles person search by integrating attribute learning to enhance local feature representation, achieving state-of-the-art results with 91.8% mAP and 93.0% rank-1 accuracy on the CUHK-SYSU dataset.
Person Search is designed to jointly solve the problems of Person Detection and Person Re-identification (Re-ID), in which the target person will be located in a large number of uncut images. Over the past few years, Person Search based on deep learning has made great progress. Visual character attributes play a key role in retrieving the query person, which has been explored in Re-ID but has been ignored in Person Search. So, we introduce attribute learning into the model, allowing the use of attribute features for retrieval task. Specifically, we propose a simple and effective model called Multi-Attribute Enhancement (MAE) which introduces attribute tags to learn local features. In addition to learning the global representation of pedestrians, it also learns the local representation, and combines the two aspects to learn robust features to promote the search performance. Additionally, we verify the effectiveness of our module on the existing benchmark dataset, CUHK-SYSU and PRW. Ultimately, our model achieves state-of-the-art among end-to-end methods, especially reaching 91.8% of mAP and 93.0% of rank-1 on CUHK-SYSU.Codes and models are available at https://github.com/chenlq123/MAE.