计算机科学 ›› 2023, Vol. 50 ›› Issue (1): 138-146.doi: 10.11896/jsjkx.211000083
李雪辉1, 张拥军1, 史殿习1,2,3, 徐化池1, 史燕燕2
LI Xuehui1, ZHANG Yongjun1, SHI Dianxi1,2,3, XU Huachi1, SHI Yanyan2
摘要： 目标跟踪作为计算机视觉领域的一个重要分支,在智能视频监控、人机交互和自动驾驶等诸多领域具有很高的研究价值。尽管目标跟踪近年来已取得较好的发展,但在复杂跟踪环境下,遮挡、目标形变、光照变化等因素仍会导致跟踪精度下降,跟踪性能不稳定。因此,提出了一种融合注意力特征的无锚框视觉目标跟踪方法(Anchor-Free object Tracking Method,AFTM)。首先,在分类和回归过程中构建自适应生成的注意力权重因子组,实现了一种高效的自适应响应图融合策略,提高了目标定位和边界框尺度计算的准确性;其次,针对数据集中样本类别不均衡的现象,使用可动态缩放的交叉熵损失作为目标定位网络的损失函数,修正模型的优化方向,使跟踪性能更加稳定可靠;最后,设计相应的学习率调整策略,对一定数量的模型进行随机权重平均,增强模型的泛化能力。公开数据集上的实验结果表明,在复杂跟踪环境下,AFTM具有更高的精度和更稳定的跟踪效果。
中图分类号:
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