Research on Multi-Target Tracking Method Based on Deep Learning

This paper investigates a deep learning-based multi-target tracking method, with the objective of addressing the challenges associated with target detection and tracking in high-density scenarios for self-driving cars. In a dynamically changing road environment, the target vehicle may become obscured, resulting in tracking failure. To address this challenge, this paper proposes a multi-sensor deep fusion technique. This technique aims to enhance the generalization of detection and tracking, thereby mitigating the impact of unexpected situations. Multi-sensor fusion technology is employed to systematically collect road information, which is then integrated with convolutional neural networks to achieve data fusion and obtain more comprehensive target information. In this paper, PreScan is utilized for the purpose of simulation and emulation experiments. The results of these experiments demonstrate that the deep learning-based multi-target tracking method can improve the generalization and accuracy of detection.