Multiple Moving Vehicles Tracking Algorithm with Attention Mechanism and Motion Model

• DOI: 10.3390/electronics13010242
• Publication Date: 2024-01-05T08:43:00Z
• Authors: Jiajun Gao; Guangjie Han; Hongbo Zhu; Lyuchao Liao
• Keywords: Tracking (education)

With the acceleration of urbanization and increasing demand for travel, current road traffic is experiencing rapid growth more complex spatio-temporal logic. Vehicle tracking on roads presents several challenges, including scenes with frequent foreground–background transitions, fast nonlinear vehicle movements, presence numerous unavoidable low-score detection boxes. In this paper, we propose AM-Vehicle-Track, following proven-effective paradigm by (TBD). At stage, introduce lightweight channel block attention mechanism (LCBAM), facilitating detector to concentrate foreground features limited computational resources. innovatively noise-adaptive extended Kalman filter (NSA-EKF) module extract vehicles’ motion information while considering impact confidence observation noise when dealing motion. Additionally, borrow Byte data association method address boxes, enabling secondary reduce ID switches. We achieve 42.2 MOTA, 51.2 IDF1, 364 IDs test set VisDrone-MOT 72 FPS. The experimental results showcase our approach’s highly competitive performance, attaining SOTA performance a speed.