An algorithm for real-time object tracking in complex environment

Dongxu Gao*, Jiangtao Cao, Zhaojie Ju

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The current sparse representation tracking algorithm is not suitable for the objects that illumination changes, scale changes, the object color is similar with the surrounding region, and occlusion etc, what's more, it is hard to realize real-time tracking for solving an l1 norm related minimization problems. An optimal algorithm is introduced by exploiting an accelerated proximal gradient approach which contains some improvements of particle filter function, sparse representation alterative weights and coefficient. These improvements not only reduce the influences of appearance change but also make the tracker runs in real time. Both qualitative and quantitative evaluations demonstrate that the proposed tracking algorithm has favorably better performance than several state-of-the-art trackers using challenging benchmark image sequences, and significantly reduces the computing cost.

Original languageEnglish
Title of host publicationProceedings of the 2014 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Pages1996-2002
Number of pages7
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 4 Sep 2014
Event2014 International Joint Conference on Neural Networks - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameIEEE IJCNN Proceedings Series
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407

Conference

Conference2014 International Joint Conference on Neural Networks
Abbreviated titleIJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

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