[1]张学英,韩广良.空间划分的目标图像识别与跟踪技术[J].华侨大学学报(自然科学版),2017,38(2):257-261.[doi:10.11830/ISSN.1000-5013.201702023]
　ZHANG Xueying,HAN Guangliang.Target Image Recognition and Tracking Technology Based on Space Partition[J].Journal of Huaqiao University(Natural Science),2017,38(2):257-261.[doi:10.11830/ISSN.1000-5013.201702023]
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空间划分的目标图像识别与跟踪技术()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第38卷
期数:
2017年第2期
页码:
257-261
栏目:
出版日期:
2017-03-20

文章信息/Info

Title:
Target Image Recognition and Tracking Technology Based on Space Partition
文章编号:
1000-5013(2017)02-0257-05
作者:
张学英1;  韩广良2
1. 河北科技师范学院 职教研究院, 河北 秦皇岛 066004;2. 中国科学院 长春光学精密机械与物理研究所, 吉林 长春 130000
Author(s):
ZHANG Xueying1;  HAN Guangliang2
1. Institute of Vocational Education, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China; 2. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130000, China
关键词:
空间划分;  图像识别;  图像定位;  跟踪算法;  分簇机制
Keywords:
space partition;  image recognition;  image location;  tracking algorithm;  clustering mechanism
分类号:
TP391
DOI:
10.11830/ISSN.1000-5013.201702023
文献标志码:
A
摘要:
针对多目标识别跟踪算法中存在的低效率、高能耗,易产生误检、漏检等问题,以跟踪精确率、能量损耗为评价指标展开研究.对目前定位与跟踪的相关算法进行分析,提出一种基于空间划分的目标图像识别与跟踪算法.利用空间分簇机制,实时收集目标的位置信息,同时,建立目标丢失与恢复机制,显著提高了目标的识别与跟踪精度,减少能量损耗.结果表明:与常规算法相比,所提算法跟踪成功率提高了10%左右,并能有效减少能量消耗,具有一定的实用价值.
Abstract:
Considering multi-object tracking algorithm exists some problem such as low efficiency, high energy loss, easy error-detection and loss dectection, we take the tracking accuracy and energy loss as the evaluation index to develop research. Based on the analysis of the current localization and tracking algorithms, a new method of object localization and tracking algorithm based on space partition is proposed, which uses the spatial clustering mechanism to collect the target location information in real time. At the same time, the object loss and restoration mechanism is established. It can improve the tracking accuracy and reduce the energy loss significantly. The simulation results show that compared with the common algorithm, the tracking success rate in our is increased by 10%, and the energy consumption is decreased effectively in this algorithm effective, and it has application value.

参考文献/References:

[1] 王栋,朱虹,康凯,等.基于背景补偿引导的动态场景下目标跟踪算法[J].仪器仪表学报,2014,35(6):1433-1440.
[2] 程宪宝.基于多群互动简化群优化的多目标跟踪算法[J].计算机工程,2016,42(3):1-7.
[3] YAZDIAN-DEHKORDI M,AZIMIFAR Z.Adaptivevisual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker[J].Visual Commum Image,2015,35(2):1-11.
[4] 郑玉凤,马秀荣,赵晓琳,等.基于颜色和边缘特征的均值迁移目标跟踪算法[J].光电子激光,2011,22(8):1231-1235.
[5] 王国良,刘金国.基于粒子滤波的多自由度运动目标跟踪[J].光学精密工程,2011,19(4):864-869.
[6] GRANSTROM K,ORGUNER U.On spawning and combination of extended/group targets modeled with random matrices[J].IEEE Transactions on Signal Processing,2013,61(3):678-692.
[7] 王秉,王子衡.非高斯噪声背景下计算机视觉目标跟踪方法[J].华侨大学学报(自然科学版),2016,37(6):774-777.
[8] 钱志鸿,朱爽,王雪.基于分簇机制的ZigBee 混合路由能量优化算法[J].计算机学报,2013,36(3):485-493.
[9] YAN Zhai,YEARY M B.A new centralized sensor fusion tracking methodology based on particle filtering for power aware systems[J].IEEE Transactions on Instrumentation and Measurement,2008,57(10):2377-2387.
[10] PENG Bao,GU Xuemai,DING Bo.Localization algorithm based on sector scan for mobile wireless sensor networks[J].Applied Mathematics and Information Sciences,2012,6(1):99-103.
[11] 邹永艳,张衍志.WSN 中视觉辅助方案结合TI CC2431 ZPS 平台的跟踪定位方法[J].电信科学,2016,32(9):68-74.
[12] 李珂,李醒飞,杨帆.IMM-UKF算法在两坐标雷达/光电融合跟踪系统的改进与应用[J].激光与光电子学进展,2016,53(12):12250.
[13] HE Tian,VICAIRE P,YAN Ting,et al.Achieving long-term surveillance in vigil net[J].ACM Transactions on Sensor Networks,2009,5(1):1-12.
[14] 田丰,郭巍,刘晓露,等.基于自适应抗差滤波的WSNs节点跟踪算法[J].传感技术学报,2011,24(1):100-105.
[15] 黄建新.室外场景下目标分割和目标识别算法[J].华侨大学学报(自然科学版),2005,26(4):353-355.
[16] 陈宝全,毛永毅.小波分析法解决无线定位跟踪问题[J].电信科学,2013,29(10):98-102.

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备注/Memo

备注/Memo:
收稿日期: 2017-02-14
通信作者: 张学英(1971-),女,讲师,博士,主要从事多媒体与计算机应用技术的研究.E-mail:tszxy@126.com.
基金项目: 国家自然科学基金资助项目(61172111)
更新日期/Last Update: 2017-03-20