Bidirectional Background Modeling for Video Surveillance

Traditional background model methods often require complicated computations, and are sensitive to illumination and shadow. In this paper, we propose a block-based background modeling method, and use our proposed method to combine color and texture characteristics. Suppression and relaxation are the two key strategies to resist illumination changes and shadow disturbance. The proposed method is quite efficient and is capable of resisting illumination changes. Experimental results show that our method is suitable for real-word scenes and real-time applications.

Diversity Sampling Based Kernel Density Estimation for Background Modeling

A novel diversity-sampling based nonparametric multi-modal background model is proposed. Using the samples having more popular and various intensity values in the training sequence, a nonparametric model is built for background subtraction. According to the related intensifies, different weights are given to the distinct samples in kernel density estimation. This avoids repeated computation using all samples, and makes computation more efficient in the evaluation phase. Experimental results show the validity of the diversity- sampling scheme and robustness of the proposed model in moving objects segmentation. The proposed algorithm can be used in outdoor surveillance systems.

Video Frame’s Background Modeling: Reviewing the Techniques

Background modeling is a technique for extracting moving objects in video frames. This technique can be used in ma-chine vision applications, such as video frame compression and monitoring. To model the background in video frames, initially, a model of scene background is constructed, then the current frame is subtracted from the background. Even-tually, the difference determines the moving objects. This paper evaluates a number of existing background modeling techniques in term of accuracy, speed and memory requirement.

On Segmentation of Moving Objects by Integrating PCA Method with the Adaptive Background Model

Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive background model is proposed by linking Gaussian mixture model with the method of principal component analysis PCA. This approach utilizes the advantage of the PCA method in providing the projections that capture the most relevant pixels for segmentation within the background models. We report the update on both the parameters of the modified method and that of the Gaussian mixture model. The obtained results show the relatively outperform of the integrated method.

Moving object detection method based on complementary multi resolution background models

A novel moving object detection method was proposed in order to adapt the difficulties caused by intermittent object motion,thermal and dynamic background sequences.Two groups of complementary Gaussian mixture models were used.The ghost and real static object could be classified by comparing the similarity of the edge images further.In each group,the multi resolution Gaussian mixture models were used and dual thresholds were applied in every resolution in order to get a complete object mask without much noise.The computational color model was also used to depress illustration variations and light shadows.The proposed method was verified by the public test sequences provided by the IEEE Change Detection Workshop and compared with three state-of-the-art methods.Experimental results demonstrate that the proposed method is better than others for all of the evaluation parameters in intermittent object motion sequences.Four and two in the seven evaluation parameters are better than the others in thermal and dynamic background sequences,respectively.The proposed method shows a relatively good performance,especially for the intermittent object motion sequences.

Optimal Insurance with Background Risk under the Ambiguity and Belief Heterogeneity Structure

In this paper, we discuss the optimal insurance in the presence of background risk while the insured is ambiguity averse and there exists belief heterogeneity between the insured and the insurer. We give the optimal insurance contract when maxing the insured’s expected utility of his/her remaining wealth under the smooth ambiguity model and the heterogeneous belief form satisfying the MHR condition. We calculate the insurance premium by using generalized Wang’s premium and also introduce a series of stochastic orders proposed by [1] to describe the relationships among the insurable risk, background risk and ambiguity parameter. We obtain the deductible insurance is the optimal insurance while they meet specific dependence structures.

Neural network based method for background modeling and detecting moving objects

This paper proposes a novel method, primarily based on the fuzzy adaptive resonance theory （ART） neural network with forgetting procedure, for moving object detection and background modeling in natural scenes. With the ability, inheriting from the ART neural network, of extracting patterns from arbitrary sequences, the background model based on the proposed method can learn new scenes quickly and accurately. To guarantee that a long-life model can derived from the proposed mothed, a forgetting procedure is employed to find the neuron that needs to be discarded and reconstructed, and the finding procedure is based on a neural network which can find the extreme value quickly. The results of a suite of quantitative and qualitative experiments conducted verify that for processes of modeling background and detecting moving objects our method is more effective than five other proven methods with which it is compared.

Research of whispered speech vocal tract system conversion based on universal background model and effective Gaussian components

Directing to the weakness of the present fixed values mapping methods （method_F）, a vocal tract system conversion method based on the universal background model （UBM） is proposed for improving the performance of the speech conversion system from Chinese whis- pered speech to normal speech. For the numerous components of UBM, the errors produced by the acoustical probability density statistical model can＇t be ignored. Thus an effective Gaus- sian mixture components chosen method based on the posterior probability summation of the minimum spectral distortion is developed to optimizing the system performance. The proposed method （method_U） is analyzed and compared using the performance index （PI） based on Itakura-Saito spectral distortion measure. It is shown experimentally that the performance of method_U is more stability for different speakers and different phonemes than that of method_F. The average PI of method_U is better than method_F. It is shown that by selecting effective Gaussian mixture components, the PI of method_U can be further improved 5.11%. Subjective auditory tests also show that the proposed method can improve the definition and intelligibility of conversion speech.

A temporal-spatial background modeling of dynamic scenes

Moving object detection in dynamic scenes is a basic task in a surveillance system for sensor data collection. In this paper, we present a powerful back- ground subtraction algorithm called Gaussian-kernel density estimator （G-KDE） that improves the accuracy and reduces the computational load. The main innovation is that we divide the changes of background into continuous and stable changes to deal with dynamic scenes and moving objects that first merge into the background, and separately model background using both KDE model and Gaussian models. To get a temporal- spatial background model, the sample selection is based on the concept of region average at the update stage. In the detection stage, neighborhood information content （NIC） is implemented which suppresses the false detection due to small and un-modeled movements in the scene. The experimental results which are generated on three separate sequences indicate that this method is well suited for precise detection of moving objects in complex scenes and it can be efficiently used in various detection systems.

A primary-secondary background model with sliding window PCA algorithm

Rain and snow seriously degrade outdoor video quality.In this work,a primary-secondary background model for removal of rain and snow is built.First,we analyze video noise and use a sliding window sequence principal component analysis de-nosing algorithm to reduce white noise in the video.Next,we apply the Gaussian mixture model(GMM)to model the video and segment all foreground objects primarily.After that,we calculate von Mises distribution of the velocity vectors and ratio of the overlapped region with referring to the result of the primary segmentation and extract the interesting object.Finally,rain and snow streaks are inpainted using the background to improve the quality of the video data.Experiments show that the proposed method can effectively suppress noise and extract interesting targets.

Dynamic background modeling using tensor representation and ant colony optimization

Background modeling and subtraction is a fundamental problem in video analysis. Many algorithms have been developed to date, but there are still some challenges in complex environments, especially dynamic scenes in which backgrounds are themselves moving, such as rippling water and swaying trees. In this paper, a novel background modeling method is proposed for dynamic scenes by combining both tensor representation and swarm intelligence. We maintain several video patches, which are naturally represented as higher order tensors,to represent the patterns of background, and utilize tensor low-rank approximation to capture the dynamic nature. Furthermore, we introduce an ant colony algorithm to improve the performance. Experimental results show that the proposed method is robust and adaptive in dynamic environments, and moving objects can be perfectly separated from the complex dynamic background.

Modeling and Generating Realistic Background Traffic by Hybrid Approach

One of the key challenges in largescale network simulation is the huge computation demand in fine-grained traffic simulation.Apart from using high-performance computing facilities and parallelism techniques,an alternative is to replace the background traffic by simplified abstract models such as fluid flows.This paper suggests a hybrid modeling approach for background traffic,which combines ON/OFF model with TCP activities.The ON/OFF model is to characterize the application activities,and the ordinary differential equations(ODEs) based on fluid flows is to describe the TCP congestion avoidance functionality.The apparent merits of this approach are(1) to accurately capture the traffic self-similarity at source level,(2) properly reflect the network dynamics,and(3) efficiently decrease the computational complexity.The experimental results show that the approach perfectly makes a proper trade-off between accuracy and complexity in background traffic simulation.

The Dynamic Location Model to Consider Background Traffic

This study concerns to the determination of location of freight distribution warehouses. It is part of a series of research projects on a distribution system we developed to deal with cases in a public service obligation state-owned company (PSO-SOC). This current research is characterized by the consideration of background traffic of the entire time period of planning rather than one certain time target on location model. It is aimed that the location decision to be more applicable and accommodative to the dynamic of the traffic condition. Once the decision is implemented, it will give the best outcome for the entire time period, not only for the initial time, end time or certain time of time period. A heuristic approach is proposed to simplify complexity of the model and network representation technique is applied to solve the model. A hyphotetical example is discussed to illustrate the mechanism of finding the optimal solution in term of both its objective function and applicability.

A numerical model study on multi-species harmful algal blooms coupled with background ecological fields

Based on systematized physical, chemical, and biological modules, a multi-species harmful algal bloom （HAB） model coupled with background ecological fields was established. This model schematically embod-ied that HAB causative algal species and the background ecological system, quantified as total biomass, were significantly different in terms of the chemical and biological processes during a HAB while the inter-action between the two was present. The model also included a competition and interaction mechanism between the HAB algal species or populations. The Droop equation was optimized by considering tempera-ture, salinity, and suspended material impact factors in the parameterization of algal growth rate with the nutrient threshold. Two HAB processes in the springs of 2004 and 2005 were simulated using this model. Both simulation results showed consistent trends with corresponding HAB processes observed in the East China Sea, which indicated the rationality of the model. This study made certain progress in modeling HABs, which has great application potential for HAB diagnosis, prediction, and prevention.

地形影响的水平相关模型在CMA-MESO中的应用

在背景误差水平相关模型中引入地形作用,研究复杂地形下近地面观测资料同化对分析和预报的影响。CMAMESO三维变分系统中背景误差水平相关关系采用高斯相关模型描述,观测信息在高度追随坐标的模式面上各向同性传播。然而在地形复杂的近地面层,观测信息传播受到山脉阻挡,因而其背景误差协方差非均匀且各向异性,观测信息传播应随地形高度变化。为此,采用美国国家气象中心NMC方法统计复杂地形下背景误差水平相关结构,构建包含地形高度和地形梯度影响的高斯相关模型,并将改进的水平相关模型应用于CMA-MESO三维变分分析。理想试验表明:考虑地形项的水平相关模型方案使观测信息以随地形高度变化的各向异性形式传播,越过大地形观测信息影响明显减弱,分析增量更加合理。我国北方一次强降水过程分析预报试验表明:随地形高度变化的水平相关模型方案使地面观测信息各向异性传播,削弱了大地形处近地面的分析增量,对降水预报略有正贡献。针对华东地区降水过程进行5 d逐小时快速更新分析预报循环试验结果表明,随地形变化的水平相关模型方案对10 m风场和24 h时效内降水预报有正贡献。

新时代背景下地质学(基地班)人才培养新模式探索

地质学(基地班)作为地球科学领域的“王牌”专业,受到中国地质大学(北京)(以下简称“我校”)的高度重视,我校为地质学(基地班)配备了最雄厚的师资力量和最优越的学习条件。然而,随着时代的发展和社会的进步,传统的培养模式已经无法完全满足新时代的地质教育需求。虽然我校已经对地质学(基地班)的培养方案进行多次修订,但是,在现有培养方案的基础上,不断探索新的培养模式,对培养方案进一步优化和完善,势在必行。该文基于作者担任我校2020级地质学(基地班)班主任期间开展的一系列探索性培养工作,进行相关总结和讨论,以期为后续相关工作的开展提供新的思路。

一种基于目标与背景特征分离模型的高光谱目标检测修正算法

高光谱图像立方体数据可以提供成像场景中地物在可见光和近红外波长范围内的空间信息和地物属性诊断的光谱特征信息,在目标检测与识别方面拥有得天独厚的天然优势。然而,基于高光谱图像数据的目标检测也存在一定缺陷,如经典的高光谱目标检测算法仅利用光谱维度信息检测目标,检测模型要么对背景高维特征矩阵构建的准确度不足,要么对背景先验光谱特征的完备性要求较高,导致算法对不同复杂度的检测场景适应性不强。因此,基于计算复杂度较低、参数需求量较少且检测性能较为优异的经典多目标检测算法—多目标约束能量最小化(MCEM),提出了一种基于目标与背景环境特征分离模型的高光谱目标检测修正算法(R-MCEM)。首先,设计了一个与目标形状、尺寸相近的逐像元移动运算窗口,依次计算窗口中的每个像元与窗口内其他像元的光谱距离之和D1,像元与各类目标的光谱距离之和D2。其次,采用获得D1/D2最小值的像元替换窗口内的所有像元值。然后,自左向右、自上而下逐像元移动窗口,重复窗口内每一个像元与目标、背景像元的光谱距离运算,并确定窗口内与背景相似度最高、与目标相似度最低的像元。直到移动运算窗口遍历整个高光谱图像,大幅提升了基于目标与背景环境特征分离的背景高维特征矩阵准确度。分别设计了基于实测高光谱图像数据和模拟图像数据的修正检测算法性能验证试验,并采用三维操作特征曲线(3D ROC)结合目标与背景分离度(SDBT)开展修正算法的检测精度评估。试验结果表明,提出的修正算法有效减少了虚警率,提高了检测精度。基于实测数据的检测精度、目标与背景分离度由MCEM算法的0.937 7、 0.57提升到R-MCEM的0.993 5、 0.67,基于模拟数据的亚像元检测能力由MCEM的20%丰度提升到R-MCEM的15%丰度。

基于TDLAS技术的甲烷气体泄漏成像检测

传统的甲烷气体泄漏检测方法主要以单点测量和红外热成像为主。前者由于测量点密度小且气体易于流动,很难确定泄漏点;后者根据气体云团与周围环境温度差异检测成像,温差较小时,成像对比度低、分辨力弱。根据以上局限性,提出一种甲烷气体泄漏成像检测技术。该技术结合TDLAS(Tunable diode laser absorption spectroscopy)遥测技术与激光主动成像方法对气体泄漏区域进行主动探测,然后采用SSIM(Structural Similarity)与混合高斯背景建模法相结合的算法对气体泄漏区域进行提取,实现对甲烷气体泄漏气团的成像检测。实验中对甲烷气体在不同泄漏流量、不同视频记录长度、不同环境下进行了检测。实验表明,该技术可实现对甲烷气体泄漏的主动成像检测,并且成像清晰、检测率高、实时性好、可准确呈现气体泄漏点。

基于灰色系统理论的我国卫生总费用之预测

利用我国2008—2020年卫生总费用及相关统计数据分别建立了GM(1,1)、FHGM(1,1)、DGM(1,1)和OFHGM(1,1)4种灰色预测模型。利用平均绝对百分比误差研究了这些模型的性能,结果表明:与GM(1,1)、FHGM(1,1)和DGM(1,1)模型相比,OFHGM(1,1)模型的预测精度最高。