结合运动平滑约束与灰度特征的卫星视频点目标跟踪

doi:10.11947/j.AGCS.2017.20160599

测绘学报 ›› 2017, Vol. 46 ›› Issue (9): 1135-1146.doi: 10.11947/j.AGCS.2017.20160599

结合运动平滑约束与灰度特征的卫星视频点目标跟踪

吴佳奇^1,2,4, 张过², 汪韬阳³, 蒋永华³

1. 辽宁工程技术大学测绘与地理科学学院, 辽宁阜新 123000;
2. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
3. 武汉大学遥感信息工程学院, 湖北武汉 430079;
4. 珠海欧比特控制工程股份有限公司, 广东珠海 519080

收稿日期:2016-11-22 修回日期:2017-07-24 出版日期:2017-09-20 发布日期:2017-10-12
通讯作者: 张过 E-mail:guozhang@whu.edu.cn
作者简介:吴佳奇(1985-),男,博士生,研究方向为卫星视频数据处理。E-mail:jiaqiwu@126.com
基金资助:
国家重点研发计划（2016YFB0500801）；国家自然科学基金（91538106；41501503；41601490；41501383）；湖北省自然科学基金（2015CFB330）；测绘遥感信息工程国家重点实验室资助项目（15E02）；地理信息工程国家重点实验室开放基金（SKLGIE2015-Z-3-1）；中央高校基本科研业务费专项资金资助（2042016kf0163）；珠海市引进创新团队项目（ZH0111-0405-160001-P-WC）

Satellite Video Point-target Tracking in Combination with Motion Smoothness Constraint and Grayscale Feature

WU Jiaqi^1,2,4, ZHANG Guo², WANG Taoyang³, JIANG Yonghua³

1. School of Geomatics, Liaoning Technical University, Fuxin 123000, China;
2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;
4. Zhuhai Orbita Control Engineering Co., Ltd., Zhuhai, 519080, China

Received:2016-11-22 Revised:2017-07-24 Online:2017-09-20 Published:2017-10-12
Supported by:
National Key Research and Development Program of China (No. 2016YFB0500801);The National Natural Science Foundation of China (Nos. 91538106;41501503;41601490;41501383);Hubei Provincial Natural Science Foundation of China (No. 2015CFB330);Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing(No. 15E02);Open Research Fund of State Key Laboratory of Geo-information Engineering(No. SKLGIE2015-Z-3-1);Fundamental Research Funds for the Central University (No. 2042016kf0163);Fund of Zhuhai Introducing Innovative Team (No. ZH0111-0405-160001-P-WC)

摘要/Abstract

摘要： 针对卫星视频条件下的点目标跟踪问题，提出了一种运动平滑约束的贝叶斯分类目标跟踪方法（BMoST）。本方法引入朴素贝叶斯分类器的思想，不依赖目标的任何先验概率，在运动平滑性约束下，利用灰度相似性特征来表达描述目标的似然度，并根据独立假设的贝叶斯定理，建立简化的分类器条件概率修正模型，通过该模型估计目标的后验概率，从而实现目标跟踪。同时，采用卡尔曼滤波辅助、优化跟踪处理，提高算法的稳健性。试验数据采用SkySat和吉林一号拍摄的视频各两段，对6个点目标进行跟踪试验。结果表明，本文提出的方法针对卫星视频的点目标跟踪效果良好，精度达到90%左右，且跟踪轨迹平滑，满足卫星视频后续高级处理和应用需要。

关键词: 卫星视频, 点目标跟踪, 贝叶斯分类, 运动平滑性, SkySat, 吉林一号

Abstract: In view of the problem of satellite video point-target tracking, a method of Bayesian classification for tracking with the constraint of motion smoothness is proposed, which named Bayesian MoST. The idea of naive Bayesian classification without relying on any prior probability of target is introduced. Under the constraint of motion smoothness, the gray level similarity feature is used to describe the likelihood of the target. And then, the simplified conditional probability correction model of classifier is created according to the independence assumption Bayes theorem. Afterwards, the tracking target position can be determined by estimating the target posterior probability on the basis of the model. Meanwhile, the Kalman filter, an assistance and optimization method, is used to enhance the robustness of tracking processing. The theoretical method proposed are validated in a number of six experiments using SkySat and JL1H video, each has two segments. The experiment results show that the BMoST method proposed have good performance, the tracking precision is about 90% and tracking trajectory is smoothing. The method could satisfy the needs of the following advanced treatment in satellite video.

Key words: satellite video, point-target tracking, Bayesian classification, motion smoothness, SkySat, JL1H

中图分类号:

TP391
P237

吴佳奇, 张过, 汪韬阳, 蒋永华. 结合运动平滑约束与灰度特征的卫星视频点目标跟踪[J]. 测绘学报, 2017, 46(9): 1135-1146.

WU Jiaqi, ZHANG Guo, WANG Taoyang, JIANG Yonghua. Satellite Video Point-target Tracking in Combination with Motion Smoothness Constraint and Grayscale Feature[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(9): 1135-1146.

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结合运动平滑约束与灰度特征的卫星视频点目标跟踪

Satellite Video Point-target Tracking in Combination with Motion Smoothness Constraint and Grayscale Feature

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