Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint|YANG Honghong;SHANG Junchao;LI Jingjing;ZHANG Yumei;WU Xiaojun|School of Computer Science,Shaanxi Normal University,Xi'an 710062,China - 期刊导航|首站-论文投稿智能助手|论文发表|论文智能投稿|期刊自助发表推荐|杂志社快速发表|查同导刊-域田数据官方网站

典型文献

Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint

文献摘要：

Vehicles or pedestrians tracking is an im-portant task in intelligent transportation system.In this paper,we propose an online multi-object tracking for in-telligent traffic platform that employs improved sparse representation and structural constraint.We first build the spatial-temporal constraint via the geometric rela-tions and appearance of tracked objects,then we con-struct a robust appearance model by incorporating the discriminative sparse representation with weight con-straint and local sparse appearance with occlusion analys-is.Finally,we complete data association by using maxim-um a posteriori in a Bayesian framework in the pursuit for the optimal detection estimation.Experimental res-ults in two challenging vehicle tracking benchmark data-sets show that the proposed method has a good tracking performance.

文献关键词：

中图分类号：

[1] 自动化技术、计算机技术（TP） / 计算技术、计算机技术（TP3） / 计算机的应用（TP39） / 信息处理(信息加工)（TP391）

[2] 自动化技术、计算机技术（TP） / 计算技术、计算机技术（TP3） / 计算机的应用（TP39） / 信息处理(信息加工)（TP391） / 模式识别与装置（TP391.4）

[3] 自动化技术、计算机技术（TP） / 自动化基础理论（TP1） / 人工智能理论（TP18） / 人工神经网络与计算（TP183）

作者姓名：

YANG Honghong;SHANG Junchao;LI Jingjing;ZHANG Yumei;WU Xiaojun

作者机构：

Key Laboratory of Modern Teaching Technology,Ministry of Education,Shaanxi Normal University,Xi'an 710062,China;School of Computer Science,Shaanxi Normal University,Xi'an 710062,China;School of Journalism and Communication,Shaanxi Normal University,Xi'an 710062,China

文献出处：

电子学报（英文）

引用格式：

[1]YANG Honghong;SHANG Junchao;LI Jingjing;ZHANG Yumei;WU Xiaojun-.Multi-Traffic Targets Tracking Based on an Improved Structural Sparse Representation with Spatial-Temporal Constraint)[J].电子学报（英文）,2022(02):266-276

A类：

telligent

B类：

Multi,Traffic,Targets,Tracking,Based,Improved,Structural,Sparse,Representation,Spatial,Temporal,Constraint,Vehicles,pedestrians,tracking,portant,task,intelligent,transportation,system,In,this,paper,online,multi,traffic,platform,that,employs,improved,sparse,representation,structural,constraint,We,first,build,spatial,temporal,via,geometric,rela,tions,appearance,tracked,objects,then,robust,model,by,incorporating,discriminative,weight,local,occlusion,analys,Finally,complete,data,association,using,maxim,um,posteriori,Bayesian,framework,pursuit,optimal,detection,estimation,Experimental,ults,two,challenging,vehicle,benchmark,sets,show,proposed,method,has,good,performance

AB值：

0.681046

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