Design of multitarget tracking and trajectory prediction algorithm in dynamic scenes

This paper proposes a multi-target tracking and trajectory prediction algorithm for dynamic scenes. By combining deep learning with generative adversarial networks (GAN), efficient target detection and motion prediction in complex environments are achieved. The algorithm first uses convolutional neural networks and long short -term memory networks to extract the spatiotemporal features of dynamic scenes, then uses the YOLO detector combined with Kalman filtering and data association technology to generate target trajectories, and finally predicts future trajectories through a GAN-based model. Experimental results show that this method performs well in multi-target tracking accuracy, identity retention rate, and trajectory prediction error, especially in target occlusion and high-density interaction scenes. It shows strong robustness. The algorithm effectively overcomes the shortcomings of traditional methods in nonlinear motion and complex dynamic environments, and provides high-performance technical support for fields such as intelligent monitoring and autonomous driving.