Fisher’s linear discriminant ratio based threshold for moving human detection in thermal video

In video surveillance, the moving human detection in thermal video is a critical phase that filters out redundant information to extract relevant information. The moving object detection is applied on thermal video because it penetrate challenging problems such as dynamic issues of background and illumination variation. In this work, we have proposed a new background subtraction method using Fisher’s linear discriminant ratio based threshold. This threshold is investigated automatically during run-time for each pixel of every sequential frame. Automatically means to avoid the involvement of external source such as programmer or user for threshold selection. This threshold provides better pixel classification at run-time. This method handles problems generated due to multiple behavior of background more accurately using Fisher’s ratio. It maximizes the separation between object pixel and the background pixel. To check the efficacy, the performance of this work is observed in terms of various parameters depicted in analysis. The experimental results and their analysis demonstrated better performance of proposed method against considered peer methods.     

基于随机背景建模的目标检测算法

运动目标检测是智能视频监控系统中的重要步骤和前提。提出了一种基于随机背景建模的非参数化建模算法,对场景中运动目标进行快速提取跟踪。在初始化阶段,从当前像素的邻域中随机抽取样本值作为背景模型;在模型更新阶段,引入了随机更新策略和背景传播机制,能够较好地抑制环境噪声;在后处理阶段,给出了一种基于积分图的前景滤波优化方法,进一步滤除噪声和填充前景空洞。实验结果表明,在复杂场景条件下,算法的目标检测性能明显优于其他几种同类算法,能够较好地抑制噪声干扰,具有较高的检测正确率。对于360288像素的测试视频,算法的计算速度高达120 f/s,完全可以满足实时应用。     

基于柯西分布的视频图像序列背景建模和运动目标检测

提出了一种用于视觉监视系统的基于柯西分布的发光模型的光照不变变化检测方法.假定视频图像序列中每个背景图像像素点灰度观测值的时序变化由白噪声引起,利用建立的初始化背景高斯统计模型对每帧图像进行归一化,得到了背景像灰度比值的分布符合标准柯西分布的结论,解决了柯西分布的模型参量估计问题.在变化检测的基础上,YCbCr颜色空间的亮度、色调和饱和度被用来识别和消除由阴影和反光等引起的变化区域.结果表明,提出的背景建模方法对场景中各种光线变化、小的背景扰动等噪声具有稳健性,可以较为可靠地检测前景目标,识别和去除阴影和反光.     

Measurements-based Moving Target Detection in Quantum Video

A method to detect a moving target in multi-channel quantum video is proposed based on multiple measurements on the video strip. The proposed method is capable of detecting the location of the moving target in each frame of the quantum video thereby ensuring that the motion trail of the object is easily and efficiently retrieved. Three experiments, i.e. moving target detection (MTD) of a pixel, MTD of an object in complex shape, and MTD of a pixel whose color is conterminous with that of its background, are implemented to demonstrate the feasibility of the proposal. This study presents a modest attempt to focus on the moving target detection and its applications in quantum video.     

A combined approach of Kullback–Leibler divergence and background subtraction for moving object detection in thermal video

In this work, a robust method for moving object detection in thermal video frames has been proposed by including Kullback–Leibler divergence (KLD) based threshold and background subtraction (BGS) technique. A trimmed-mean based background model has been developed that is capable enough to reduce noise or dynamic component of the background. This work assumed that each pixel has normally distributed. The KLD has computed between background pixel and a current pixel with the help of Gaussian mixture model. The proposed threshold is useful enough to classify the state of each pixel. The post-processing step uses morphological tool for edge linking, and then the flood-fill algorithm has applied for hole-filling, and finally the silhouette of targeted object has generated. The proposed methods run faster and have validated over various real-time based problematic thermal video sequences. In the experimental results, the average value of F₁-score, area under the curve, the percentage of correct classification, Matthew’s correlation coefficient show higher values whereas total error and percentage of the wrong classification show minimum values. Moreover, the proposed-1 method achieved higher accuracy and execution speed with minimum false alarm rate that has been compared with proposed-2 as well as considered peer methods in the real-time thermal video.     

基于像素序列形态的适应性背景重构算法

背景重构是视频图像处理领域的支撑性工作之一.针对传统的背景重构算法运算复杂、背景图像失真等不足,本文提出了一种像素序列形态适应性背景重构算法.该算法通过提取像素序列形态特征进行分类处理,不同形态适用独立的背景提取策略、背景更新时刻和背景更新策略.实验结果验证表明:该算法无需对视频场景中的背景和运动目标建立模型,可直接从一组含有运动前景的视频图像中准确地重构背景,并有效避免混合现象|背景缓慢变化和突变时,亦可快速有效地完成背景重构.     

基于YCbCr颜色空间背景建模与手势阴影消除

为了提高动态手势检测的精确度,本文将基于YCbCr颜色空间的混合高斯背景建模应用于动态手势识别中,并且提出手势阴影消除的有效算法。首先,对待检测视频帧通过抠图抠出手势图像,在YCb'Cr'颜色空间进行椭圆拟合,统计建立椭圆肤色模型,继而在YCbCr颜色空间进行混合高斯背景建模检测出动态手势,点乘原图像得到含有阴影的RGB手势图像,对检测出的含有阴影的手势图像利用已建立的椭圆肤色模型进行阴影消除,最后将手势图像连成视频序列。实验结果表明,该算法在复杂背景下进行动态手势的检测率可达91.4%,高出传统方法10%左右,能够满足动态手势检测基本要求,且具有较高的实用价值。     

基于FPGA的运动目标实时检测跟踪算法及其实现技术

运动目标检测跟踪有关的算法及其基于PC平台的实现已经比较成熟,但实时性较差。将采集的彩色视频流分成灰度和彩色两个数据流,灰度视频用于目标检测,彩色视频流用于跟踪显示。以经典的帧间差分法和背景差分法为基础,根据现场可编程门阵列(FPGA)的特点及片外同步动态存储器的存取控制要求,对这两个算法用FPGA逻辑单元进行了设计和实现。对原始彩色视频流和转换后的灰度视频流的存取使用乒乓操作,在滤波和形态学处理时使用了并行的流水线操作,极大地提高了算法的实时处理能力。在FPGA开发板上构建了一个彩色视频图像中运动目标检测跟踪系统,对系统性能进行了测试。实验结果表明,系统可在多种分辨率和帧率下进行运动目标进行实时检测跟踪;固定背景差分法对目标运动速度无限制,但当使用帧差法对快速运动目标进行有效的检测时,应使目标的帧差间距大于3.2像素。     

Complex background suppression based on fusion of morphological Open filter and nucleus similar pixels bilateral filter

To reduce the influences of the heavy clutter on infrared small target detection, a new background suppression algorithm is presented in this paper which depends on fusion of two different filters. The Nucleus Similarity Degree (NSD) of each pixel is analyzed first, then morphological Open filter which favors point target enhancement and the Nucleus Similar Pixels Bilateral Filter (NSPBF) which favors background prediction are fused. The complex background suppression and target enhancement can be accomplished more effectively by the fusion. Experimental results indicates that the method is efficient for background suppression under the condition of heavy clutter.     

拉普拉斯约束低秩表示的高光谱图像异常检测

伴随高光谱图像的广泛使用,高光谱图像技术得到长足的发展,其中高光谱图像异常检测技术越发受到重视。为了解决传统高光谱图像异常检测技术的实用性和检测效果不佳的问题,提出一种新颖的低秩表示检测算法。对于高光谱图像,大部分背景像元均可以被少量主要的背景像元组合近似地表示,且它们的表示系数将会位于低秩的空间中。在剩下无法被主要背景像元表示的稀疏部分中存在着异常像元,则可以被检测算法提取出来。在低秩表示中,背景像元字典的构建将会影响高光谱图像中背景像元的表示。如直接从现有高光谱图像中提取背景像元构建字典,会导致异常像元对背景像元字典的污染。而利用待检测高光谱图像观测数据和由光谱组成原理可合成的潜在未观测数据来构建背景像元字典,提取出背景像元的主要特征,有利于更好地分离出稀疏异常像元的信息。并且高光谱图像数据存在高维几何结构特点,通过引入拉普拉斯矩阵来约束空间中局部相似的像元对于待检测像元的表示作用,获得更接近于真实的表示系数。实验结果分别在仿真数据和真实数据上验证,与传统方法相比,提出的方法通过有效地突出异常像元提高了检出率和抑制了背景像元,降低了误检率。     

Background subtraction based on time-series clustering and statistical modeling

This paper proposes a robust method to detect and extract silhouettes of foreground objects from a video sequence of a static camera based on the improved background subtraction technique. The proposed method analyses statistically the pixel history as time series observations. The proposed method presents a robust technique to detect motions based on kernel density estimation. Two consecutive stages of the k-means clustering algorithm are utilized to identify the most reliable background regions and decrease the detection of false positives. Pixel and object based updating mechanism for the background model is presented to cope with challenges like gradual and sudden illumination changes, ghost appearance, non-stationary background objects, and moving objects that remain stable for more than the half of the training period. Experimental results show the efficiency and the robustness of the proposed method to detect and extract the silhouettes of moving objects in outdoor and indoor environments compared with conventional methods.     

Target extraction from blurred trace infrared images with a superstring galaxy template algorithm

Accurate and efficient targets extraction from blurred trace infrared images has very important meaning for latent trace evidence collection in crime scene. Based on the superstring theory, a superstring galaxy template extraction algorithm for infrared trace target is presented. First, all of the pixels are divided into three classes: target pixels, background pixels and blurred pixels. Next, the superstring template characteristics for every pixel in a blurred infrared image are calculated as the features of each pixel. Finally, a galaxy covering algorithm is proposed, target pixels and background pixels are used for training the galaxy covering domain of every galaxy classifiers, and these classifiers will divide each blurred pixel into two classes: a target pixel or a background pixel. Experimental results indicate that the superstring galaxy template algorithm can improve the target extraction rate and reduce the extraction error rate.     

一种背景误差累积的高光谱图像异常检测算法

针对背景信息对高光谱图像异常检测的强干扰问题,提出了一种背景误差累积的异常检测算法.该算法通过构造背景子空间,利用各像素到该子空间的正交投影,得到有效抑制背景并突显目标的背景误差数据,用于后续的异常目标检测.最后用真实的高光谱数据进行仿真实验,理论分析比较和实验结果表明该算法具有较好的检测效果.     

Hybrid multi-resolution detection of moving targets in infrared imagery

A hybrid moving target detection approach in multi-resolution framework for thermal infrared imagery is presented. Background subtraction and optical flow methods are widely used to detect moving targets. However, each method has some pros and cons which limits the performance. Conventional background subtraction is affected by dynamic noise and partial extraction of targets. Fast independent component analysis based background subtraction is efficient for target detection in infrared image sequences; however the noise increases for small targets. Well known motion detection method is optical flow. Still the method produces partial detection for low textured images and also computationally expensive due to gradient calculation for each pixel location. The synergistic approach of conventional background subtraction, fast independent component analysis and optical flow methods at different resolutions provide promising detection of targets with reduced time complexity. The dynamic background noise is compensated by the background update. The methodology is validated with benchmark infrared image datasets as well as experimentally generated infrared image sequences of moving targets in the field under various conditions of varying illumination, ambience temperature and the distance of the target from the sensor location. The significant value of F-measure validates the efficiency of the proposed methodology with high confidence of detection and low false alarms.     

The moving target detection algorithm based on the improved visual background extraction

This paper presents a moving target detection algorithm based on the improved visual background extraction. Traditional VIBE (Visual Background Extractor) algorithm is one of the powerful background subtraction algorithm. It can quickly, accurately and integrally detect moving target. However, sometimes it will falsely determine background as foreground and impact detection results. In this paper, we improve the traditional VIBE algorithm by joining TOM (Time of map) mechanism in the process of detection, so it can not only use the pixel’s spatial domain information, but also make full use of the pixel’s time domain information. Experiments detailed in this paper show the algorithm presented in this paper has better detection effect than the traditional VIBE algorithm.     

快速无参数背景建模的红外目标检测方法研究

针对复杂背景红外图像序列目标检测的难题,给出了一种用于红外监控系统中入侵目标检测的背景建模方法。应用特征样本集为每一个像素建立统计无参数样本集模型,根据核函数估计计算每一个像素值对模型的符合概率。使用双阈值进行目标检测和模型更新,将图像分为三类:可靠背景、感兴趣区域和不可靠背景。通过不可靠背景类提供的信息进一步将感兴趣区域细分为入侵目标和错误检测。对几种红外图像序列仿真实验表明,该算法不仅可以比较精确的检测显著入侵目标,对于容易淹没在噪声中的弱小入侵目标也可以实现准确地检测。     

Three-Layer Detection Pixel of Single-Photon Thermoelectric Detector Based on Rare-Earth Hexaborides

The results of computer simulation of heat propagation processes in the three-layer detection pixel of single-photon thermoelectric detector after the absorption of single photons with the energies 0.5–4.13 eV are presented. The various geometries of the detection pixel consisting of rareearth hexaborides are considered. The lanthanum hexaboride (LaB₆) is chosen as the absorber material, and for the materials of thermoelectric sensor the cerium (CeB₆) and lanthanum–cerium (La_0.99Ce_0.01) B6 hexaborides are chosen. The problem is solved to achieve the high system efficiency of thermoelectric detector for the detection of photons in the wavelength range from the UV to the near IR. The computer modeling was carried out based on the equation of heat propagation from the limited volume with the use of three-dimensional matrix method for differential equations. It is shown that a single-photon thermoelectric detector with a three-layer detection pixel made only of hexaborides will have the gigahertz count rate, high-energy resolution, and detection efficiency exceeding 90%. Taking into account the advantages of the three-layer detection pixel compared to the single-layer it can be argued that the three-layer detection pixel of the thermoelectric detector has the great prospects to solve a number of single-photon detection tasks.     

一种新的空谱联合探测高光谱影像目标探测算法

高光谱遥感影像不但具有高分辨率的空间信息还包含连续的光谱信息,因此在目标探测领域具有独特的应用优势。传统的高光谱遥感影像目标探测侧重于光谱信息的应用,形成了确定性算法和统计学算法。确定性算法通过计算目标光谱与待检测光谱之间的距离来查找目标,不能检测亚像素目标,而且容易受到噪声的影响;统计学目标检测计算背景统计特性,通过探测异常点来检测目标,可以检测亚像素目标和小目标,但容易受到目标尺寸的影响,不能很好的检测大目标。随着高光谱遥感影像的空间分辨率的增加,探测目标已有亚像素目标逐步转换为单像素及多像素目标,此时,在高光谱图像中,相同类别的地物在空间分布上呈现聚类特性, 因此,在利用高光谱遥感影像进行目标探测时,需要将其空间信息融入算法中。将空间特征引入传统目标探测算法。提出了一种新的空谱结合的高光谱目标探测算法,将传统的基于统计的目标探测算子与空域邻域聚类算法相结合,首先利用目标探测算子将影像划分为潜在目标区域与背景区域;通过计算潜在目标区域的质心,以质心为中心进行邻域聚类,剔除潜在目标区域中的背景区域,通过迭代计算获取最终目标探测结果。传统的基于统计的目标探测算子,将整个探测区域定义为背景区域,实现对背景区域的统计特征提取,而该方法将背景区域与潜在目标区域分离,剔除了目标区域对背景区域的统计干扰。将本算子与传统的约束能量最小化算子和自适应余弦探测算子进行分析比较可知,该算子的大目标探测性能优于传统的统计算子。     

A 128 × 128 Pixel Complementary Metal Oxide Semiconductor Image Sensor with an Improved Pixel Architecture for Detecting Modulated Light Signals

A complementary metal oxide semiconductor (CMOS) image sensor for the detection of modulated light under background illumination has been developed. When an object is illuminated by a modulated light source under background illumination the sensor enables the object alone to be captured. This paper describes improvements in pixel architecture for reducing fixed pattern noise (FPN) and improving the sensitivity of the image sensor. The improved 128 × 128 pixel CMOS image sensor with a column parallel analog-to-digital converter (ADC) circuit was fabricated using 0.35-mm CMOS technology. The resulting captured images are shown and the properties of improved pixel architecture are described. The image sensor has FPN of 1/28 that of the previous image sensor and an improved pixel architecture comprising a common in-pixel amp and a correlated double sampling (CDS) circuit. The use of a split photogate increases the sensitivity of the image sensor to 1.3 times that of the previous image sensor.     

基于光谱角累加的高光谱图像异常检测算法

针对传统方法中用作背景的像素中存在干扰像素的问题,提出一种基于光谱角累加的高光谱图像异常检测算法。通过计算测试区域待测像素光谱向量与其他光谱向量之间的夹角,并将其累加,得到图像中每个像素的异常程度;然后使用波段选择预处理方法进一步提高检测性能。HyMap高光谱数据验证表明,在虚警概率设为0.008时,检测概率达到0.73,即在提高异常检测可靠度的同时,降低了虚警概率。