A multitask joint framework for real-time person search

Person searches generally involve three important parts: person detection, feature extraction and identity comparison. However, a search integrating comparison has the two following drawbacks. First, accuracy of detection will affect Second, it is difficult to achieve real-time results in real-world applications. To solve these problems, we propose multitask joint framework for (MJF) that optimizes For module, YOLOv5-GS model, which trained with dataset. combines advantages Ghostnet squeeze-and-excitation block improves speed detection. design model adaptation architecture, can select different networks according number people. It balance relationship between speed. comparison, 3D pooled table matching strategy improve identification accuracy. On condition 1920 $$\times$$ 1080-resolution video 200-ID table, IR FPS achieved by our method reach 82.69% 25.14, respectively. Therefore, MJF search.