[1]钟必能,潘胜男.选择性搜索和多深度学习模型融合的目标跟踪[J].华侨大学学报(自然科学版),2016,37(2):207-212.[doi:10.11830/ISSN.1000-5013.2016.02.0207]
　ZHONG Bineng,PAN Shengnan.Multi-Clue Fusion Target Tracking Algorithm Based on Selective Search and Deep Learning[J].Journal of Huaqiao University(Natural Science),2016,37(2):207-212.[doi:10.11830/ISSN.1000-5013.2016.02.0207]
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选择性搜索和多深度学习模型融合的目标跟踪()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第37卷
期数:
2016年第2期
页码:
207-212
栏目:
出版日期:
2016-03-20

文章信息/Info

Title:
Multi-Clue Fusion Target Tracking Algorithm Based on Selective Search and Deep Learning
文章编号:
1000-5013(2016)02-0207-06
作者:
钟必能1; 2;  潘胜男1; 2
1. 华侨大学 计算机科学与技术学院, 福建 厦门 361021;2. 华侨大学 计算机视觉与模式识别重点实验室, 福建 厦门 361021
Author(s):
ZHONG Bineng1; 2;  PAN Shengnan1; 2
1. College of Computer Science and Technology, Huaqiao University, Xiamen 361021, China; 2. Computer Vision and Pattern Recognition Laboratory, Huaqiao University, Xiamen 361021, China
关键词:
目标跟踪;  深度学习;  多模型融合;  选择性搜索;  评价指标
Keywords:
object tracking;  deep learning;  multi-clue fusion;  selective search;  evaluating indicator
分类号:
TP301
DOI:
10.11830/ISSN.1000-5013.2016.02.0207
文献标志码:
A
摘要:
提出一种基于深度学习的多模型(卷积神经网络和卷积深信度网络)融合目标跟踪算法.该算法在提取候选粒子方面,使用选择性搜索和粒子滤波的方法.CVPR2013跟踪评价指标(50个视频序列、30个跟踪算法)验证了:该算法在跟踪中能有效地缓解目标物体由于遮挡、光照变化和尺度变化等因素造成的跟踪丢失情况的发生.
Abstract:
A multi-clue tracking algorithm(convolutional neural network and convolutional deep belief network)based on deep learning was proposed. The algorithm used selective search and particle filtering method in extracting candidate particles. CVPR2013 tracking benchmark(50 video sequences, 30 tracking algorithms)verifies: the algorithm can ease the loss of tracking due to the occlusion, the change of illumination and size etc.

参考文献/References:

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[8] HUANG G B,LEE H,LEARNED-MILLER E.Learning hierarchical representations for face verification with convolutional deep belief[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2012,8(6):1836-1844.
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备注/Memo

备注/Memo:
收稿日期: 2015-06-16
通信作者: 钟必能(1981-),男,副教授,博士,主要从事计算机视觉、模式识别、目标跟踪方面的研究.E-mail:bnzhong@hqu.edu.cn.
基金项目: 国家自然科学基金资助项目(61202299); 国家自然科学基金面上资助项目(61572205); 福建省自然科学基金资助项目(2015J01257); 福建省高校杰出青年科研人才培育计划项目(JA13007)
更新日期/Last Update: 2016-03-20