时空方向能量特征用于红外弱表观目标跟踪

张乐; 安志勇; 郝永平; 岱钦

doi:doi:10.3788/gzxb20174608.0810001

光子学报, 2017, 46 (8): 0810001, 网络出版: 2017-10-30

时空方向能量特征用于红外弱表观目标跟踪

Infrared Weak-Feature Target Tracking Using the Spatial-Temporal Orientation Energy Feature

论文大纲

张乐 ^1,2,*安志勇 ¹郝永平 ²岱钦 ²

作者单位

¹ 长春理工大学光电工程学院, 长春 130022

² 沈阳理工大学装备工程学院, 沈阳 110168

摘要

在红外弱表观目标跟踪中，由于目标表观信息贫乏且受场景强噪声干扰，现有算法难以有效提取目标特征，实现对目标的准确跟踪.针对这一问题，本文提出一种基于时空方向能量的红外弱表观目标跟踪方法.该方法提取目标时空方向能量作为特征，建立方向能量直方图描述目标特征.在粒子滤波方法框架下，将粒子方向能量直方图与目标模板的相似度作为粒子滤波预测与更新阶段的观测输入.由于时空方向能量来源于目标的运动特征，因此本文方法具有较强的抗辐射突变性.实验结果证明，该方法能够稳定地跟踪弱表观目标，与传统基于灰度特征的跟踪方法相比，可靠性增强，且具有良好的适应性.

Abstract

For the task of weak-feature target tracking in infrared image sequences, existing methods failed to track the target accurately for lack of information in the target′s surface and the interferences from the strong noise in the scene. Focusing on this problem, a particle filter method is proposed by the Spatial-Temporal Orientation Energy (SOE). The method describes the target by using the SOE histogram. The similarity of the SOE histograms between the particles and the target is employed as the observation input to the particle filter. It is noted that the method is high robustness to target illuminated radiation as the SOE feature reflects target dynamics. Experimental results show that the proposed method has good stability to track targets in different scenes and is more effective and adaptive than the traditional grey-scale feature.

参考文献

[1] 陶霖密，李亮，邸慧军. 无表观特征小目标检测与跟踪［J］. 中国图象图形学报，2012,17(3):357-364.

TAO Lin-mi, LI Liang, DI Hui-jun. Featureless small object detection and tracking［J］. Journal of Image and Graphics, 2012, 17(3): 357-364.

[2] MILA N, ROTH A S, SCHINDLER K . Continuous energy minimization for multitarget tracking［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2014, 36(1): 58.

[3] TRAN D, YUAN J, FORSYTH D. Video event detection: from subvolume localization to spatiotemporal path search［J］. IEEE Transactions on Software Engineering, 2013, 36(2): 404-16.

[4] 吴昊, 陈树新, 杨宾峰, 等. 鲁棒的高斯和容积卡尔曼滤波红外目标跟踪算法［J］. 红外与毫米波学报, 2016, 35(1): 123-128.

WU Hao, CHEN Shu-xin, YANG Bin-feng, et al. Robust improved Gaussian-sum cubature Kalman filter for infrared target tracking［J］. Journal of Infrared Millim Waves, 2016, 35(1): 123-128.

[5] TEONG J, YOON T S, PARK J B. Mean shift tracker combined with online learning-based detector and kalman filtering for real-time tracking［J］. Expert Systems with Applications, 2017, 79: 194-206.

[6] 刘行, 陈莹. 自适应多特征融合目标跟踪［J］. 光电工程, 2016, 43(3): 58-65.

LIU Xing, CHEN Ying. Target tracking based on adaptive fusion of multi-future［J］. Opto-Electronic Engineering, 2016, 43(3): 58-65.

[7] 王暐，王春平，李军，等. 特征融合和模型自适应更新相结合的相关滤波目标跟踪［J］. 光学精密工程, 2016, 24(8): 2059-2066.

WANG Wei, WANG Chun-ping, LI Jun, et al. Correlation filter tracking based on feature fusing and model adaptive updating［J］. Optics and Precision Engineering, 2016, 24(8): 2059-2066.

[8] KHAKPOUR S, PAZZI R W, EI-KHATIB K. Using clustering for target tracking in vehicular ad hoc networks［J］. Vehicular Communications, 2017, 9: 83-96.

[9] 王寿峰，白俊奇. 带宽自适应的均值漂移红外目标跟踪算法［J］. 光子学报，2014,43(5)：0510003.

WANG Shou-feng, BAI Jun-qi. Bandwidth-adaptive mean shift infrared target tracking algorithm［J］. Acta Photonica Sinca, 2014, 43(5): 0510003.

[10] CANNON K , WILDES R. Spatiotemporal oriented energy features for visual tracking［A］. in Asian Conference on Computer Vision. 2007.

[11] ADELSON E H, BERGEN J R . Spatiotemporal energy models for the perception of motion［J］. Journal of the Optical Society of America ,(1917-1983), 1985, 73(2): 284-99.

[12] CANNON K, GRYN J M, WILDES R P. Visual tracking using a pixelwise spatiotemporal oriented energy representation［A］. in Computer Vision - ECCV 2010, European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, 2010.

[13] DERPANIS K G , GRYN J M. Three-dimensional nth derivative of Gaussian separable steerable filters［C］. The International Conference on Image Processing, 2005. 3: III - 553-6.

[14] ANDERSON C H , BURT P J, WAL G S V D. Change detection and tracking using pyramid transform techniques ［J］. Intelligent Robots and Computer Vision, 1985, 579: 72-78.

[15] FREEMAN W T, ADELSON E H. The design and use of steerable filters［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 1991, 13(9): 891-906.

张乐, 安志勇, 郝永平, 岱钦. 时空方向能量特征用于红外弱表观目标跟踪[J]. 光子学报, 2017, 46(8): 0810001. ZHANG Le, AN Zhi-yong, HAO Yong-ping, DAI Qin. Infrared Weak-Feature Target Tracking Using the Spatial-Temporal Orientation Energy Feature[J]. ACTA PHOTONICA SINICA, 2017, 46(8): 0810001.

时空方向能量特征用于红外弱表观目标跟踪

关于本站 Cookie 的使用提示

全站搜索

时空方向能量特征用于红外弱表观目标跟踪

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索