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.
作者
李嘉琪
LI Jiaqi(School of Film,Modern College of Northwest University,Xi'an Shaanxi 710130,China)
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
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
computer vision
target tracking
object detection
bounding box estimation
Discriminant Correlation Filter(DCF)
