Visual tracking based on convolutional deep belief network

Dan Hu, Xingshe Zhou, Junjie Wu

科研成果: 书/报告/会议事项章节会议稿件同行评审

3 引用 (Scopus)

摘要

Visual tracking is an important task within the field of computer vision. Recently, deep neural networks have gained significant attention thanks to their success on learning image features. But the existing deep neural networks applied in visual tracking are fullconnected complicated architectures with large amount of redundant parameters that would be low efficiently to learn. We tackle this problem by using a novel convolutional deep belief network (CDBN) with convolution, weights sharing and pooling to have much fewer parameters to learn, in addition to gain translation invariance which would benefit the tracker performance. Theoretical analysis and experimental evaluations on an open tracker benchmark demonstrate our CDBN based tracker is more accurate by improving tracking success rate 22.6% and tracking precision 62.8% on average, while maintaining low computation cost by reduces the number of parameters to 44.4%, compared to DLT, another well-known deep learning tracker. Meanwhile, our tracker can achieve real-time performance by a graphics processing unit (GPU) speedup of 2.61 times on average and up to 3.08 times.

源语言英语
主期刊名Advanced Parallel Processing Technologies - 11th International Symposium, APPT 2015, Proceedings
编辑Qing Ji, Yunji Chen, Paolo Ienne
出版商Springer Verlag
103-115
页数13
ISBN(印刷版)9783319232157
DOI
出版状态已出版 - 2015
活动11th International Symposium on Advanced Parallel Processing Technologies, APPT 2015 - Jinan, 中国
期限: 20 8月 201521 8月 2015

出版系列

姓名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9231
ISSN(印刷版)0302-9743
ISSN(电子版)1611-3349

会议

会议11th International Symposium on Advanced Parallel Processing Technologies, APPT 2015
国家/地区中国
Jinan
时期20/08/1521/08/15

指纹

探究 'Visual tracking based on convolutional deep belief network' 的科研主题。它们共同构成独一无二的指纹。

引用此