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	Dim and Small Target Tracking Using an Improved Particle Filter Based on Adaptive Feature Fusion 期刊论文 OAI收割 ELECTRONICS, 2022, 卷号: 11, 期号: 15 作者: Huo, Youhui; Chen, Yaohong; Zhang, Hongbo; Zhang, Haifeng; Wang, Hao \| 收藏 \| 浏览/下载：51/0 \| 提交时间：2022/09/28 dim and small target target tracking feature fusion particle filter resampling method
	Infrared small target tracking algorithm based on temporal-spatial structure sparse Bayesian estimation 期刊论文 OAI收割 Infrared Physics & Technology, 2019, 卷号: 105, 期号: 3, 页码: 103106-1-14 作者: Li, Zhengzhou; Chen, Cheng; Liu, Depeng; Zhang, Chao; Zeng, Jingjie \| 收藏 \| 浏览/下载：98/0 \| 提交时间：2021/05/11 Sparse Representation Bayesian Inference Temporal-spatial Structure Prediction Structure Information Small Target Tracking
	Infrared Dim and Small Target Tracking Method Incorporating Statistical Characteristics 会议论文 OAI收割 International Symposium on Infrared Technology and Application / International Symposium on Robot Sensing and Advanced Control, Beijing, PEOPLES R CHINA, 2016-05-09 作者: Zhang T(张涛); Guo, Hongwei 收藏 \| 浏览/下载：36/0 \| 提交时间：2017/04/14 Dim and small target Statistical characteristics Target modeling Tracking
	A matching algorithm on statistical properties of Harris corner (EI CONFERENCE) 会议论文 OAI收割 2011 International Conference on Information and Automation, ICIA 2011, June 6, 2011 - June 8, 2011, Shenzhen, China 作者: He B. 收藏 \| 浏览/下载：21/0 \| 提交时间：2013/03/25 The fundamental goal of target recognition and video tracking is to match target template with source image. Most matching methods are based on image intensity or multi-feature points. And the latter method is more popular for its high accuracy and small calculation. Image Registration Based on Feature Points focus on effective feature extraction of image points and paradigm. Harris corner in the image rotation gray noise and viewpoint change conditions has an ideal match results is more recent application of one feature point. This paper extract the Harris corner deviation and covariance firstly experiments show that the two features exclusive then applied them to image registration for the first time. A set of actual images have shown this proposed method not only overcomes the complicated background gray uneven distribution problems but also pan and zoom the image has a good resistance. 2011 IEEE.
	Research on infrared dim-point target detection and tracking under sea-sky-line complex background (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China 作者: Dong Y.-X.; Zhang H.-B.; Li Y.; Li Y.; Li Y. 收藏 \| 浏览/下载：111/0 \| 提交时间：2013/03/25 Target detection and tracking technology in infrared image is an important part of modern military defense system. Infrared dim-point targets detection and recognition under complex background is a difficulty and important strategic value and challenging research topic. The main objects that carrier-borne infrared vigilance system detected are sea-skimming aircrafts and missiles. Due to the characteristics of wide field of view of vigilance system the target is usually under the sea clutter. Detection and recognition of the target will be taken great difficulties.There are some traditional point target detection algorithms such as adaptive background prediction detecting method. When background has dispersion-decreasing structure the traditional target detection algorithms would be more useful. But when the background has large gray gradient such as sea-sky-line sea waves etc.The bigger false-alarm rate will be taken in these local area.It could not obtain satisfactory results. Because dim-point target itself does not have obvious geometry or texture feature in our opinion from the perspective of mathematics the detection of dim-point targets in image is about singular function analysis.And from the perspective image processing analysis the judgment of isolated singularity in the image is key problem. The foregoing points for dim-point targets detection its essence is a separation of target and background of different singularity characteristics.The image from infrared sensor usually accompanied by different kinds of noise. These external noises could be caused by the complicated background or from the sensor itself. The noise might affect target detection and tracking. Therefore the purpose of the image preprocessing is to reduce the effects from noise also to raise the SNR of image and to increase the contrast of target and background. According to the low sea-skimming infrared flying small target characteristics the median filter is used to eliminate noise improve signal-to-noise ratio then the multi-point multi-storey vertical Sobel algorithm will be used to detect the sea-sky-line so that we can segment sea and sky in the image. Finally using centroid tracking method to capture and trace target. This method has been successfully used to trace target under the sea-sky complex background. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).
	基于直方图处理和新型相似性度量函数的小尺寸目标跟踪算法期刊论文 OAI收割信号处理, 2010, 卷号: 26, 期号: 6, 页码: 891-897 陈建军; 安国成; 张索非; 吴镇扬收藏 \| 浏览/下载：17/0 \| 提交时间：2011/05/23 小尺寸目标跟踪均值移动直方图处理相似性度量权值计算Small target tracking Mean shift Histogram processing Similarity measure Weight computing
	Novel approach for tracking and recognizing dim small moving targets based on probabilistic data association filter 期刊论文 OAI收割 OPTICAL ENGINEERING, 2007, 卷号: 46, 期号: 1 作者: Li, Zhengzhou; Jin, Gang; Dong, Nengli 收藏 \| 浏览/下载：19/0 \| 提交时间：2015/09/21 dim and small moving target target recognition and tracking probabilistic data association filter multifeature fusion
	Detecting of multi-target in sea or sky background based on wavelet energy (EI CONFERENCE) 会议论文 OAI收割 Advanced Sensor Systems and Applications II, November 8, 2004 - November 12, 2004, Beijing, China 作者: Liu G. 收藏 \| 浏览/下载：19/0 \| 提交时间：2013/03/25 The technology of multi-target tracking is always detecting small targets in complex background. According to the frequency characteristic of nature background and small target a method of detecting targets based on wavelet energy is suggested. The energy of targets in horizontal and vertical direction outclass that of background or we can say the gray degree of targets outclass that of background. Then through selecting adaptive region-value targets and background can be divided. By the method of classifying the figure centers of every target can be calculated. According to the figure centers of every target in several neighboring frames the position and velocity of every target in next frame can be estimated by Kalman filter. Experiment results have shown that small targets in sea or sky nature background can be detected by this method and they can be tracking.
	The advanced position compensation to improve the dynamic tracking ability for fast moving target in an optoelectronic tracking system (EI CONFERENCE) 会议论文 OAI收割 ICO20: Optical Devices and Instruments, August 21, 2005 - August 26, 2005, Changchun, China 作者: Zhao L.; Chen J. 收藏 \| 浏览/下载：18/0 \| 提交时间：2013/03/25 The servo control system of the optoelectronic tracking equipment usually is a kind of SISO. When the fast moving target is tracked the over-tune of the servo system is the main representation for the dynamic tracking error. As the result the tracking ability may be improved by limiting the over-tune. We put forward a method the advanced position compensation (called as APC in short) which is to check the speed-overtune by applying the advanced position information. For the large accelerate target small over-tune tracking is achieved but it lowers the ability for tracking the sine signal at low frequency area. While the dynamic high-type can improve the tracking precision for the sine signal at low frequency area we work out a brand-new method which combines the advantages of the both. It increases the tracking precision in the whole frequency band at large scale for the optoelectronic tracking system. The simulation results show that when the target moves with the largest accelerate 120/s2 120/s the maximum static tracking error is about 0.6.