Abstract: To address the issues of low accuracy and difficulty in achieving tracking localization in current vehicular binocular vision systems, a pedestrian localization and tracking method based on improved YOLOv5s is proposed in this study. Target localization and tracking functionalities are designed in the binocular vision-based YOLOv5 object detection network. Additionally, a Transformer-based adaptive fusion attention mechanism is incorporated into the main network to enhance target feature representation. The network structure of the feature fusion module is modified to improve fusion accuracy. The improved YOLOv5s model is named YOLOv5sBCT, which exhibits approximately 4.20% enhancement in precision and about 2.91% improvement in mAP@0.5:0.95 compared to the baseline YOLOv5s model. Furthermore, the measured distance error rate is reduced by approximately 6.19%. Consequently, this study provides an effective method for enhancing pedestrian tracking localization accuracy in vehicular intelligent systems, thereby enhancing driver safety and driving performance and promoting advancements in the field of intelligent transportation.

Key words: pedestrian localization; YOLOv5; fusion attention mechanism; mAP@0.5:0.95

摘要： 针对目前车载双目视觉系统获取行人定位方法精度不高和难以实现跟踪定位的问题， 提出了一种基于改进 YOLOv5s 的行人定位跟踪方法。研究在基于双目视觉的 YOLOv5 目标 检测网络中设计目标定位跟踪功能；在模型主干网络中设计基于 Transformer 的自适应融合注 意力机制以提高目标特征表示能力；修改特征融合模块的网络结构以提高融合精度。将改进 的 YOLOv5s 模型命名为 YOLOv5sBCT，相较于基准模型 YOLOv5s 在训练结果的精确率上提 升约 4.20%，在 mAP@0.5:0.95 中提升约 2.91%，在实际测量的距离误差率降低约 6.19%。因 此，文章针对车载行人定位感知方法能够为车载智能系统提供一种有效提升行人跟踪定位精 度的方法，以提升驾驶员的驾驶安全性，推动智能交通领域的发展。

关键词: 行人定位；YOLOv5；融合注意力机制；mAP@0.5:0.95