  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,
