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

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外文摘要：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.

外文关键词：

Intelligent vehiclesMulti-traffic ob-ject trackingSparse representationSpatial-temporal constraint

作者：

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

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作者单位：

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

基金：

国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金Young Talent Fund of University Association for Science and Technology in Shaanxi中国博士后科学基金Natural Science Foundation of Shaanxi ProvincialNatural Science Foundation of Shaanxi Provincial

项目编号：

61907028118720361177217861971273202001052018M6409502019J Q-5742019GY-217

出版年：

2022

DOI：

10.1049/cje.2020.00.007

电子学报(英文)

CSTPCDSCIEI

ISSN：1022-4653

年,卷(期)：2022.31(2)

参考文献量3