论文标题
实时人员搜索的多任务联合框架
A Multi-task Joint Framework for Real-time Person Search
论文作者
论文摘要
人员搜索通常涉及三个重要部分：人检测，提取和身份比较。但是，人搜索集成检测，提取和比较具有以下缺点。首先，检测的准确性将影响比较的准确性。其次，在现实世界应用中很难实现实时。为了解决这些问题，我们提出了一个用于实时人员搜索（MJF）的多任务联合框架，该框架分别优化了人的检测，特征提取和身份比较。对于人员检测模块，我们提出了Yolov5-GS模型，该模型是由人数据集培训的。它结合了幽灵网和挤压和兴奋（SE）块的优势，并提高了速度和准确性。对于特征提取模块，我们设计模型适应体系结构（MAA），可以根据人数选择不同的网络。它可以平衡准确性和速度之间的关系。为了进行身份比较，我们提出了一个三维（3D）汇总表和提高识别精度的匹配策略。在1920年*1080分辨率视频和500个IDS表的情况下，我们方法实现的标识率（IR）和每秒（fps）的框架可以达到93.6％和25.7，
Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult to achieve real-time in real-world applications. To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively. For the person detection module, we proposed the YOLOv5-GS model, which is trained with person dataset. It combines the advantages of the Ghostnet and the Squeeze-and-Excitation (SE) block, and improves the speed and accuracy. For the feature extraction module, we design the Model Adaptation Architecture (MAA), which could select different network according to the number of people. It could balance the relationship between accuracy and speed. For identity comparison, we propose a Three Dimension (3D) Pooled Table and a matching strategy to improve identification accuracy. On the condition of 1920*1080 resolution video and 500 IDs table, the identification rate (IR) and frames per second (FPS) achieved by our method could reach 93.6% and 25.7,
