Visible and infrared tracking based on multi-view multi-kernel fusion model

In the visual tracking problem, fusion of visible and infrared sensors provides complementarily useful features and can consistently help distinguish the target from the background efficiently. Recently, multi-view learning has received growing attention due to its enormous potential in combining diverse view features containing consistent and complementary characteristics. Therefore, in this paper, a visible and infrared fusion tracking algorithm based on multi-view multi-kernel fusion (MVMKF) model is presented. The proposed MVMKF model considers the diversities of visible and infrared views and embeds complementary information from them. Furthermore, the multi-kernel framework is used to learn the importance of view features so that an integrated appearance representation is made with regard to the respective performance. Besides, the tracking task is completed with naive Bayes classifier in sophisticated compressive feature domain, considering the high performances of classifier-level and sophisticated feature-level learning for multiple views. The experimental results demonstrate that the MVMKF tracking algorithm performs well in terms of accuracy and robustness.     

Fusion of the low-light-level visible and infrared images for night-vision context enhancement

For better night-vision applications using the low-light-level visible and infrared imaging, a fusion framework for night-vision context enhancement(FNCE) method is proposed. An adaptive brightness stretching method is first proposed for enhancing the visible image. Then, a hybrid multi-scale decomposition with edge-preserving filtering is proposed to decompose the source images. Finally, the fused result is obtained via a combination of the decomposed images in three different rules. Experimental results demonstrate that the FNCE method has better performance on the details(edges), the contrast, the sharpness, and the human visual perception. Therefore,better results for the night-vision context enhancement can be achieved.     

一种多传感器目标跟踪的数据融合方法

提出了一种多传感器目标跟踪数据融合方法,先对各传感器信号作经Kalman滤波,分别得到目标的航迹,再对各航迹进行数据融合,以提高目标的定位精度。该方法直接对经Kalman滤波后得到的直角坐标下航迹进行数据融合,避免了各个传感器之间繁琐的几何位置转换,又由于经Kalman滤波直接得到滤波后航迹的后验误差,从而避免了复杂的误差转换,具有简洁、高效等特点。     

基于非下采样Shearlet变换耦合相对亮度测度的可见光与红外图像融合算法

为了克服当前较多可见光与红外图像融合方法在通过图像能量特征融合图像时,忽略了图像的全局相对亮度特征,导致融合图像存在红外目标表达能力弱等问题,文章引入非下采样Shearlet变换,通过测量图像的相对亮度来融合图像.借助NSST变换来处理输入图像,解析出图像的低频和高频系数;采用区域能量函数,对区域图像所含的能量特征进行...     

Object tracking in a stereo and infrared vision system

In this paper, we deal with the problem of real-time detection, recognition and tracking of moving objects in open and unknown environments using an infrared (IR) and visible vision system. A thermo-camera and two stereo visible-cameras synchronized are used to acquire multi-source information: three-dimensional data about target geometry and its thermal information are combined to improve the robustness of the tracking procedure. Firstly, target detection is performed by extracting its characteristic features from the images and then by storing the computed parameters on a specific database; secondly, the tracking task is carried on using two different computational approaches. A Hierarchical Artificial Neural Network (HANN) is used during active tracking for the recognition of the actual target, while, when partial occlusions or masking occur, a database retrieval method is used to support the search of the correct target followed. A prototype has been tested on case studies regarding the identification and tracking of animals moving at night in an open environment, and the surveillance of known scenes for unauthorized access control.     

Robust infrared target tracking using discriminative and generative approaches

The process of designing an efficient tracker for thermal infrared imagery is one of the most challenging tasks in computer vision. Although a lot of advancement has been achieved in RGB videos over the decades, textureless and colorless properties of objects in thermal imagery pose hard constraints in the design of an efficient tracker. Tracking of an object using a single feature or a technique often fails to achieve greater accuracy. Here, we propose an effective method to track an object in infrared imagery based on a combination of discriminative and generative approaches. The discriminative technique makes use of two complementary methods such as kernelized correlation filter with spatial feature and AdaBoost classifier with pixel intesity features to operate in parallel. After obtaining optimized locations through discriminative approaches, the generative technique is applied to determine the best target location using a linear search method. Unlike the baseline algorithms, the proposed method estimates the scale of the target by Lucas-Kanade homography estimation. To evaluate the proposed method, extensive experiments are conducted on 17 challenging infrared image sequences obtained from LTIR dataset and a significant improvement of mean distance precision and mean overlap precision is accomplished as compared with the existing trackers. Further, a quantitative and qualitative assessment of the proposed approach with the state-of-the-art trackers is illustrated to clearly demonstrate an overall increase in performance.     

红外全景扫描跟踪成像系统设计与实现

针对红外搜索跟踪系统中全范围快速搜索和跟踪成像视场不同的问题,采用线列探测器配合高速转台和高均匀性扫描器,设计实现了一种360全方位搜索和跟踪的成像系统。实验结果表明,该系统成功实现了360全景扫描,在搜索到目标后可快速转入跟踪成像,由搜索转入跟踪成像状态的稳定时间小于1.94 s,系统性能指标达到实用要求。     

激光聚变冲击波点火的热斑形成机制

通过对冲击波点火内爆过程的数值模拟分析点火热斑压缩及形成机制。分析了传统中心点火的内爆过程,热斑主要经历冲击波压缩和惯性压缩过程,点火主要通过惯性压缩来实现。并仔细分析了冲击波点火的内爆压缩过程,从内爆角度来看冲击波点火并不是压缩和点火分开的两步过程,点火冲击波实际参与压缩过程,点火冲击波对热斑的直接影响很有限,热斑仍然主要通过壳层的惯性压缩实现点火。利用惯性压缩的定标关系及冲击波碰撞对壳层影响规律分析了热斑增压的物理机制,冲击波点火是通过点火冲击波与回冲击波的碰撞来提高壳层的密度,从而实现热斑压力的提升。     

激光聚变冲击波点火的热斑形成机制

通过对冲击波点火内爆过程的数值模拟分析点火热斑压缩及形成机制。分析了传统中心点火的内爆过程,热斑主要经历冲击波压缩和惯性压缩过程,点火主要通过惯性压缩来实现。并仔细分析了冲击波点火的内爆压缩过程,从内爆角度来看冲击波点火并不是压缩和点火分开的两步过程,点火冲击波实际参与压缩过程,点火冲击波对热斑的直接影响很有限,热斑仍然主要通过壳层的惯性压缩实现点火。利用惯性压缩的定标关系及冲击波碰撞对壳层影响规律分析了热斑增压的物理机制,冲击波点火是通过点火冲击波与回冲击波的碰撞来提高壳层的密度,从而实现热斑压力的提升。     

基于音视频信息融合的目标检测与跟踪算法

针对单一视觉跟踪算法易受遮挡影响的缺陷,提出一种基于音视频信息融合的目标检测与跟踪算法。整个算法框架包括视频检测与跟踪、声源定位、音视频信息融合跟踪3个模块。视频检测与跟踪模块采用YOLOv5m算法作为视觉检测的框架,使用无迹卡尔曼滤波和匈牙利算法实现多目标的跟踪与匹配;声源定位模块采用十字型麦克风阵列获取音频信息,结合各麦克风接收信号的时延计算声源方位;音视频信息融合跟踪模块构建音视频似然函数和音视频重要性采样函数,采用重要性粒子滤波作为音视频融合跟踪的算法,实现对目标的跟踪。在室内复杂环境下对算法性能进行测试,结果表明该算法跟踪准确率达到90.68%,相较于单一模态算法具有更好的性能。     

Detection and tracking of targets in infrared images using Bayesian techniques

Simple yet robust techniques for detecting targets in infrared (IR) images are an important component of automatic target recognition (ATR) systems. In our previous works, we have developed IR target detection and tracking algorithms based on image correlation and intensity. In this paper, we discuss these algorithms, their performances and problems associated with them and then propose novel algorithms to alleviate these problems. Our proposed target detection and tracking algorithms are based on frequency domain correlation and Bayesian probabilistic techniques, respectively. The proposed algorithms are found to be suitable for real-time detection and tracking of static or moving targets, while accommodating for detrimental affects posed by the clutter and background noise. Finally, limitations of all these algorithms are discussed.     

One color contrast enhanced infrared and visible image fusion method

Color constancy (Toet and Franken, 2003 [2]; Toet, 2003 [7]) and color contrast (Scribner et al., 2000 [21]; Lee et al., 2005 [23]) are two important topics for color image fusion. The paper focuses on the low color contrast problem of linear fusion algorithms with color transfer method. Color transfer technology is popular in infrared (IR) and visible image fusion to give the fused image a natural day-time color appearance (Toet, 2003 [7]; Wang et al., 2007 [8]; Zheng and Essock, 2008 [9]). However, in the color transfer step, all three channels of the color space are processed with the same linear mapping without color enhancement, resulting in low color contrast between the target and the background (Wang et al., 2007 [8]). Based on the characteristics of the IR image, we introduce a ratio of local to global divergence of the IR image to improve the color contrast. The enhancement ratios for both hot and cold targets are larger than one, while it tends to one for the background. As a result, the proposed method pops out both hot and cold targets in color, where hot targets will appear intense red, and cold targets will appear cyan. Subjective results show visible color contrast enhancement effects. Target detection experiments through hue and saturation components of the fused image show an improvement in the hit rate for target detection, owing to larger color distance between the target and the background.     

红外昼夜跟瞄系统工作波段选择与分析

针对红外系统设计中工作波段选择问题,从探测器性能、光学弥散斑、大气传输特性、目标与背景辐射特性、海杂波干扰等多个方面对中波和长波红外进行了对比分析;制定了详细的中波和长波红外热像仪昼夜探测效果摸底试验方案,得出在不同时间、不同气象条件下2种探测器对相同目标的探测试验数据。结合试验数据与理论分析,提出红外系统在用于对岸远程昼夜探测情况下波段选择的建议。     

Color-image encryption scheme based on channel fusion and spherical diffraction

A secure encryption scheme for color images based on channel fusion and spherical diffraction is proposed in this paper. In the proposed encryption scheme, a channel fusion technology based on the discrete wavelet transformation is used to transform color images into single-channel grayscale images, firstly. In the process of transformation, the hyperchaotic system is used to permutate and diffuse the information of red—green—blue (RGB) channels to reduce the correlation of channels. Then the fused image is encrypted by spherical diffraction transform. Finally, the complex-valued diffraction result is decomposed into two real parts by the improved equal module decomposition, which are the ciphertext and the private key. Compared with the traditional color image encryption schemes that encrypt RGB channels separately, the proposed scheme is highly secure and robust.     

Multifocus image fusion and denoising scheme based on homogeneity similarity

A novel image fusion algorithm based on homogeneity similarity is proposed in this paper, aiming at solving the fusion problem of clean and noisy multifocus images. Firstly, the initial fused image is acquired with one multiresolution image fusion method. The pixels of the source images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions are determined. In order to improve the fusion performance, morphological opening and closing are employed for post-processing. Secondly, the homogeneity similarity is introduced and used to fuse the clean and noisy multifocus images. Finally, the fused image is obtained by weighting the neighborhood pixels of the point of source images which are located at the focused region. Experimental results demonstrate that, for the clean multifocus image fusion, the proposed method performs better than some popular image fusion methods in both subjective and objective qualities. Furthermore, it can simultaneously resolve the image restoration and fusion problem when the source multifocus images are corrupted by the Gaussian white noise, and can also provide better performance than the conventional methods.     

Multifocus image fusion scheme using focused region detection and multiresolution

To solve the fusion problem of the multifocus images of the same scene, a novel algorithm based on focused region detection and multiresolution is proposed. In order to integrate the advantages of spatial domain-based fusion methods and transformed domain-based fusion methods, we use a technique of focused region detection and a new fusion method of multiscale transform (MST) to guide pixel combination. Firstly, the initial fused image is acquired with a novel multiresolution image fusion method. The pixels of the original images, which are similar to the corresponding initial fused image pixels, are considered to be located in the sharply focused regions. By this method, the initial focused regions can be determined, and the techniques of morphological opening and closing are employed for post-processing. Then the pixels within the focused regions in each source image are selected as the pixels of the fused image; meanwhile, the initial fused image pixels which are located at the focused border regions are retained as the pixels of the final fused image. The fused image is then obtained. The experimental results show that the proposed fusion approach is effective and performs better in fusing multi-focus images than some current methods.     

Robust visual tracking base on adaptively multi-feature fusion and particle filter

In order to avoid the tracking failure based on single feature under the conditions of cluttered backgrounds illumination changes, a robust tracking algorithm was proposed based on adaptively multi-feature fusion and particle filter. Color histogram was used to describe the overall distribution characteristics of the target and histogram of oriented gradients containing some construction information and LBP is very effective to describe the image texture features. The Three features were fused in the frame of particle filter. Meanwhile, the weights of each feature were adjusted dynamically. The experimental results show that with adaptive fusion, the tracker becomes more robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion.     

Monolithic integration technology between microlens arrays and infrared charge coupled devices

In this paper, we develop an integration technology between Si microlens and 256(H)×256(V) element PtSi Schottky-barrier infrared charge coupled device (IR-CCD) to improve the optical responsivity of CCD sensor. The refractive microlenses with the pixel size of approximately 28×28 μm² is directly fabricated on the backside of CCD substrate to focus the incident irradiation onto the active area. For the integration device the fill factor is improved by a factor of 2.1. As a result, the IR-CCD image sensors operating at 77 K indicate an approximate 0.06–0.4 increase in relative optical responsivity in the spectral range of from 1 to 5 μm. CCD imaging quality with microlens has been improved comparing to that without microlens to a great extent.