共查询到18条相似文献,搜索用时 46 毫秒
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评估每个粒子的重要性是确保粒子滤波法跟踪目标准确性的重要因素。针对背景杂波和噪声干扰形成的大量虚警导致小弱目标跟踪识别的随机性和不确定性问题, 提出了一种基于粒子区别性稀疏表征的小弱目标跟踪方法。该方法根据红外图像信号自适应构建分类超完备字典, 即反映目标信号特征的目标字典和表示背景杂波的背景字典, 有利于突出目标粒子和背景粒子在联合分类字典的稀疏表征差异程度;建立基于目标粒子和背景粒子稀疏重构残差差异性的粒子滤波观测模型, 采用随机估计法对字典子空间进行在线更新, 实现对目标状态估计与跟踪。理论分析和试验结果表明, 该方法增强了随机粒子的状态估计能力, 提升了粒子稀疏表征对小弱运动目标的适应能力和跟踪识别准确度。 相似文献
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《光学技术》2021,47(5):622-631
为了提高背景杂波和成像噪声等干扰下红外小目标的跟踪精度,提出了一种基于结构信息建模和判别稀疏的红外小目标跟踪方法。小目标信号在广义高斯目标超完备字典上被稀疏分解,以便从受噪声干扰和杂波污染的红外图像中提取出小目标的空间结构信息;设计了转移受限粒子滤波跟踪算法,以提高粒子的采样概率;在转移受限粒子滤波框架下,基于判别稀疏表示和L_1范数最小化框架求解候选目标的稀疏系数,实现小目标的跟踪。基于各种红外序列对所提方法进行实验论证,实验结果表明,所提方法能够在杂波和噪声较大的干扰下稳定地跟踪小目标,其中心误差、重叠率和平均视频播放帧率分别为3pixel、0.7和40fps,均优于其他对比方法,且具有较强的鲁棒性。 相似文献
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针对红外目标在跟踪中计算复杂的问题,构建辅助粒子滤波算法。利用贝叶斯重要性采样算法,在权值大的粒子基础上引入辅助粒子变量,然后重新定义重要采样分布函数,防止重采样后粒子概率密度变化。两次加权计算,使粒子权值比仅用重采样的粒子权值变化更稳定,采样点最接近真实状态;同时不同权值粒子的概率阈值可作为粒子滤波是否完成的判断准则。在二维平面构造红外运动目标模型中,系统为零均值高斯白噪声。仿真数据表明:该算法在x,y方向的均方误差、画面处理时间、RM SE性能上优于粒子滤波算法和重采样粒子滤波算法。 相似文献
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目前,基于稀疏表示的目标跟踪通常为在目标模板集上重构候选样本的正向模型或者在候选样本集上描述目标模板的反向模型.两个模型的共同点是均需计算候选样本与模板集合之间的稀疏相关系数矩阵.基于此,建立了一个双向联合稀疏表示的跟踪模型,该模型通过L2范数约束正反向稀疏相关系数矩阵达到一致收敛.与之前的单向稀疏表示模型相比,双向稀疏表示跟踪模型在正反向联合求解框架下可以更加充分地挖掘所有候选样本与模板集之间的稀疏映射关系,并将稀疏映射表上对正负模板区分度最好的候选样本作为目标.基于加速逼近梯度(accelerated proximal gradient)快速算法,以矩阵形式推导了双向稀疏表示模型的求解框架,使得候选样本集和目标模板集均以矩阵方式并行求解,在一定程度上提高了计算效率.实验数据表明所提出的算法优于传统的单向稀疏表示目标跟踪算法. 相似文献
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基于形态学与遗传粒子滤波器的红外小目标检测与跟踪算法 总被引:2,自引:1,他引:1
针对复杂背景下红外小目标的检测与跟踪,提出了一种融合了top-hat算法、遗传算法以及粒子滤波器的新方法.该方法首先采用提取副帧的方法去除目标周围部分的背景和噪音,有效地减少了参与运算的像素数目;其次,将具有不同边缘特性的多个结构体应用于top-hat检测算法中,提高了副帧中预目标的有效性;接着,利用目标时空运动的相关性,结合阈值判断来去除虚假目标,增强了目标检测的可靠性;最后,将遗传算法引入粒子滤波算法,较好地改善了粒子的多样性,在保障跟踪实时性的同时,提高了粒子滤波的准确度.实验结果表明,该算法在检测准确度、跟踪准确度和鲁棒性都具有一定的优越性. 相似文献
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针对粒子算法存在的问题,提出了辅助粒子算法.该算法在重采样算法基础上,引进辅助变量,对粒子的权2次计算,可使粒子权值比重采样的粒子权值变化更稳定,最后给出了红外目标模型和均方根误差函数.仿真结果表明该算法对运动目标跟踪的均值和方差上均优于标准粒子滤波、重采样粒子滤波,且提高了计算效率. 相似文献
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The small dim moving target usually submerged in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio (SNR). A target tracking algorithm based on particle filter and discriminative sparse representation is proposed in this paper to cope with the uncertainty of dim moving target tracking. The weight of every particle is the crucial factor to ensuring the accuracy of dim target tracking for particle filter (PF) that can achieve excellent performance even under the situation of non-linear and non-Gaussian motion. In discriminative over-complete dictionary constructed according to image sequence, the target dictionary describes target signal and the background dictionary embeds background clutter. The difference between target particle and background particle is enhanced to a great extent, and the weight of every particle is then measured by means of the residual after reconstruction using the prescribed number of target atoms and their corresponding coefficients. The movement state of dim moving target is then estimated and finally tracked by these weighted particles. Meanwhile, the subspace of over-complete dictionary is updated online by the stochastic estimation algorithm. Some experiments are induced and the experimental results show the proposed algorithm could improve the performance of moving target tracking by enhancing the consistency between the posteriori probability distribution and the moving target state. 相似文献
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Infrared search and track technology for small target plays an important role in infrared warning and guidance. In view of the tacking randomness and uncertainty caused by background clutter and noise interference, a robust tracking method for infrared small target based on sample constrained particle filtering and sparse representation is proposed in this paper. Firstly, to distinguish the normal region and interference region in target sub-blocks, we introduce a binary support vector, and combine it with the target sparse representation model, after which a particle filtering observation model based on sparse reconstruction error differences between sample targets is developed. Secondly, we utilize saliency extraction to obtain the high frequency area in infrared image, and make it as a priori knowledge of the transition probability model to limit the particle filtering sampling process. Lastly, the tracking result is brought about via target state estimation and the Bayesian posteriori probability calculation. Theoretical analyses and experimental results show that our method can enhance the state estimation ability of stochastic particles, improve the sparse representation adaptabilities for infrared small targets, and optimize the tracking accuracy for infrared small moving targets. 相似文献
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提出了一种基于空时联合稀疏重构的红外小弱运动目标检测算法。通过学习序列图像内容而构建的空时联合字典能同时刻画目标或背景的形态特征和运动信息;利用多元高斯运动模式从空时联合字典中提取出目标空时字典和背景空时字典,目标空时过完备字典描述移动的目标,背景空时过完备字典表征背景噪声。将连续多帧图像在空时联合字典上进行稀疏分解,然后分别利用目标空时字典和背景空时字典中的最大稀疏系数及其空时原子重构信号,获取重构残余能量差异来区分目标和背景。试验结果表明,由同源的空时字典重构的残余能量小,而由异构的空时字典恢复的残余能量大,该方法不仅能提高序列信号表示的稀疏度,还能有效提高小运动目标的探测能力。 相似文献
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提出了一种基于空时联合稀疏重构的红外小弱运动目标检测算法。通过学习序列图像内容而构建的空时联合字典能同时刻画目标或背景的形态特征和运动信息;利用多元高斯运动模式从空时联合字典中提取出目标空时字典和背景空时字典,目标空时过完备字典描述移动的目标,背景空时过完备字典表征背景噪声。将连续多帧图像在空时联合字典上进行稀疏分解,然后分别利用目标空时字典和背景空时字典中的最大稀疏系数及其空时原子重构信号,获取重构残余能量差异来区分目标和背景。试验结果表明,由同源的空时字典重构的残余能量小,而由异构的空时字典恢复的残余能量大,该方法不仅能提高序列信号表示的稀疏度,还能有效提高小运动目标的探测能力。 相似文献
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The infrared and visible image fusion algorithm based on target separation and sparse representation
Although the fused image of the infrared and visible image takes advantage of their complementary, the artifact of infrared targets and vague edges seriously interfere the fusion effect. To solve these problems, a fusion method based on infrared target extraction and sparse representation is proposed. Firstly, the infrared target is detected and separated from the background rely on the regional statistical properties. Secondly, DENCLUE (the kernel density estimation clustering method) is used to classify the source images into the target region and the background region, and the infrared target region is accurately located in the infrared image. Then the background regions of the source images are trained by Kernel Singular Value Decomposition (KSVD) dictionary to get their sparse representation, the details information is retained and the background noise is suppressed. Finally, fusion rules are built to select the fusion coefficients of two regions and coefficients are reconstructed to get the fused image. The fused image based on the proposed method not only contains a clear outline of the infrared target, but also has rich detail information. 相似文献