| Citation: |
CAI Ruhua, ZHOU Jianbin, WU Sunyong, ZHENG Xiangfei. An Adaptive Tracking Algorithm for Infrared Dim Small Targets in Complex Scenes Based on GLMB Filter[J]. Infrared Technology , 2024, 46(7): 743-753.
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In this study, we propose a robust adaptive tracking algorithm for infrared dim objects that addresses the problem of tracking discontinuities and failures caused by missed detections and clutter in complex scenes. In the pre-processing stage, an algorithm that measures the image complexity eliminates unnecessary calculations. This algorithm determines the scene type by calculating multiple features of the infrared image to obtain the scene complexity, and then selects the corresponding detection algorithm to extract the target candidate location, grayscale and local histogram features. Subsequently, a measurement model and likelihood function are established based on the scene type. In the tracking stage, to flexibly match the filtering parameters of the generalized labeled multi-Bernoulli (GLMB) filter, an adaptive algorithm suitable for video image distribution is proposed for track initiation. Aiming at the unknown detection probability of an infrared image sequence, a cardinality probability hypothesis density (CPHD) filter was integrated into the GLMB to estimate the detection probability of the target in real time, thereby improving the accuracy of the tracker. The simulation results show that the proposed algorithm can effectively track small infrared objects in different complex scenarios.
| [1] |
张雪, 梁晓庚. 红外探测器发展需求[J]. 电光与控制, 2013, 20(2): 41-45. https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201302013.htm
ZHANG Xue, LIANG Xiaogeng. Development of and demands for infrared detectors[J]. Electronics Optics & Control, 2013, 20(2): 41-45. https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201302013.htm
|
| [2] |
赵琰, 刘荻, 赵凌君, 等. 基于YOLOv3的复杂环境红外弱小目标检测[J]. 航空兵器, 2019, 26(6): 29-34. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906006.htm
ZHAO Yan, LIU Di, ZHAO Lingjun, et al. Infrared dim and small target detection based on YOLOv3 in complex environment[J]. Aero Weapony, 2019, 26(6): 29-34. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906006.htm
|
| [3] |
CHEN C L, LI H, WEI Y, et al. A local contrast method for small infrared target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(1): 574-581.
|
| [4] |
Aghaziyarati S, Moradi S, Talebi H. Small infrared target detection using absolute average difference weighted by cumulative directional derivatives[J]. Infrared Physics & Technology, 2019, 101: 78-87.
|
| [5] |
FU J, ZHANG H, WEI H, et al. Small bounding-box filter for small target detection[J]. Optical Engineering, 2021, 60(3): 1-14.
|
| [6] |
薛秋条, 宁巧娇, 吴孙勇, 等. 基于JMS-SMC-PHD滤波的检测前跟踪算法[J]. 红外技术, 2020, 42(8): 783-788. http://hwjs.nvir.cn/cn/article/id/hwjs202008013
XUE Qiutiao, NING Qiaojiao, WU Sunyong, et al. A track-before-detect algorithm based on a JMS-SMC-PHD filter[J]. Infrared Technoiogy, 2020, 42(8): 783-788. http://hwjs.nvir.cn/cn/article/id/hwjs202008013
|
| [7] |
Bocquel M. Random finite sets in multi-target tracking-efficient sequential MCMC implementation[D]. Enschede: Centre for Telematics and Information Technology, 2013: 21-22.
|
| [8] |
Kim D Y, Vo B N, Vo B T. A labeled random finite set online multi-object tracker for video data[J]. Pattern Recognition, 2019, 90: 377-389. DOI: 10.1016/j.patcog.2019.02.004
|
| [9] |
XIAO S, MA Y, FAN F, et al. Tracking small targets in infrared image sequences under complex environmental conditions[J]. Infrared Physics & Technology, 2020, 104: 103102.
|
| [10] |
TAO H, SHEN X, DENG Q. Infrared target tracking algorithm based on Bernoulli filter and support vector machine[C]//International Conference on Information Science and Education (ICISE-IE). IEEE, 2020: 277-281.
|
| [11] |
董小虎, 傅瑞罡, 高颖慧, 等. 复杂背景下红外小目标自适应检测跟踪[J]. 航空兵器, 2019, 26(6): 22-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906005.htm
DONG Xiaohu, FU Ruigang, GAO Yinghui, et al. Detection and tracking of small infrared targets adaptively in complex background[J]. Aero Weaponry, 2019, 26(6): 22-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906005.htm
|
| [12] |
SHU C, DING X, FANG C. Histogram of the oriented gradient for face recognition[J]. Tsinghua Science and Technology, 2011, 16(2): 216-224. DOI: 10.1016/S1007-0214(11)70032-3
|
| [13] |
Eysa R, Hamdulla A. Issues on infrared dim small target detection and tracking[C]//International Conference on Smart Grid and Electrical Automation (ICSGEA). IEEE, 2019: 452-456.
|
| [14] |
施天俊, 鲍广震, 王福海, 等. 一种适用于多场景的红外弱小目标检测跟踪算法[J]. 航空兵器, 2020, 26(6): 35-42. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906007.htm
SHI Tianjun, BAO Guangzhen, WANG Fuhai, et al. An infrared small target detection and tracking algorithm applying for multiple scenarios[J]. Aero Weaponry, 2020, 26(6): 35-42. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906007.htm
|
| [15] |
Brown C E. Coefficient of Variation[M]. Berlin: Springer, 1998: 155-157.
|
| [16] |
Horé A, Ziou D. Image quality metrics: PSNR vs. SSIM[C]//20th International Conference on Pattern Recognition, 2010: 2366-2369.
|
| [17] |
Dubuisson Séverine. Tracking with Particle Filter for High-dimensional Observation and State Spaces[M]. New York: John Wiley & Sons, Inc., 2015.
|
| [18] |
Derpanis K G. The bhattacharyya measure[J]. Mendeley Computer, 2008, 1(4): 1990-1992.
|
| [19] |
Mahler R P S, Vo B T, Vo B N. CPHD filtering with unknown clutter rate and detection profile[J]. IEEE Transactions on Signal Processing, 2011, 59(8): 3497-3513. DOI: 10.1109/TSP.2011.2128316
|
| [20] |
Reuter S, Vo B T, Vo B N, et al. The labeled multi-Bernoulli filter[J]. IEEE Transactions on Signal Processing, 2014, 62(12): 3246-3260. DOI: 10.1109/TSP.2014.2323064
|
| [21] |
Do C T, Nguyen T T D, et al. Multi-object tracking with an adaptive generalized labeled multi-Bernoulli filter[J]. Signal Processing, 2022, 196: 1-20.
|
| [22] |
HUI B W, SONG Z Y, FAN H Q, et al. A dataset for dim small target detection and tracking of aircraft in infrared image sequences [DB/OL]. Science Data Bank, 2022 [2022-09-01]. https://www.scidb.cn/en/.
|
| [23] |
刘洁, 李小昱, 李培武, 等. 基于近红外光谱的板栗水分检测方法[J]. 农业工程学报, 2010, 26(2): 338-341. https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201002059.htm
LIU Jie, LI Xiaoyu, LI Peiwu, et al. Determination of moisture in chestnuts using near infrared spectroscopy[J]. Transaction of the Chinese Society of Agricultural Engineering, 2010, 26(2): 338-341. https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201002059.htm
|
| [24] |
Schuhmacher D, Vo B T, Vo B N. A consistent metric for performance evaluation of multi-object filters[J]. IEEE Transactions on Signal Processing, 2007, 56(8): 3447-3457.
|
| [25] |
HAO Q, GAO H, JUN G. Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics[J]. Chinese Journal of Aeronautics, 2016, 29(5): 1378-1384. DOI: 10.1016/j.cja.2016.05.002
|
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