基于深度学习的视频序列运动目标自适应跟踪  

Adaptive tracking of moving targets in video sequences based on deep learning

作　　者：李嘉琪 LI Jiaqi(School of Film,Modern College of Northwest University,Xi'an Shaanxi 710130,China)

机构地区：[1]西北大学现代学院电影学院,陕西西安710130

出　　处：《太赫兹科学与电子信息学报》2024年第11期1304-1311,共8页Journal of Terahertz Science and Electronic Information Technology

摘　　要：针对视频序列中外观变化、背景杂波和严重遮挡等因素导致的目标跟踪精确度低的问题,提出一种新型的双阶段自适应跟踪模型。该模型包含目标检测和边界框估计2个阶段:在目标检测阶段,模型对目标进行粗略定位;在边界框估计阶段,精确确定目标位置。为应对视频场景复杂性及小目标跟踪的挑战,采用了多特征融合技术构建丰富的目标表示。实验结果表明,与在线和实时跟踪(SORT)、Tracktor++、FairMOT、Transformer等模型相比,本模型表现出最优的综合性能,有效平衡了计算速度与跟踪精确度之间的关系,展现出良好的应用潜力。In response to the issues of low tracking accuracy in video sequences due to factors such as appearance changes,background clutter,and severe occlusions,a novel two-stage adaptive tracking model is proposed.This model includes two phases:target detection and bounding box estimation.In the target detection phase,the model roughly locates the target;in the bounding box estimation phase,the exact position of the target is determined.To address the complexity of video scenes and the challenges of tracking small targets,multi-feature fusion technology is employed to construct a rich target representation.Experimental results show that compared with models such as Simple Online and Realtime Tracking(SORT),Tracktor++,FairMOT,and Transformer,this model demonstrates the best overall performance,effectively balancing the relationship between computational speed and tracking accuracy,and showing good potential for application.

关 键 词：计算机视觉 目标跟踪 目标检测 边界框估计 判别相关滤波器 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]