海事监控视频舰船目标检测研究现状与展望

舰船目标检测是海域监控、港口流量统计、舰船身份识别以及行为分析与取证等智能海事应用的基石。随着我国海洋强国建设的推进,智慧航运和智慧海洋工程迅速发展,对通过海事监控视频开展有效的舰船目标检测识别以确保航运和海洋工程安全的需求日益紧迫。本文针对基于海事监控视频的舰船目标检测任务,回顾了舰船目标检测数据集及性能评价指标、基于传统机器学习和基于卷积神经网络的深度学习的目标检测方法等方面的国内外研究现状,分析了海洋环境中舰船目标检测任务面临的舰船目标尺度的多样性、舰船类别的多样性、海洋气象的复杂性、水面的动态性、相机的运动性和图像的低质量等技术难点,并通过实验验证,在多尺度特征融合、数据增广和能耗降低等方面提出了舰船目标检测的优化方法;同时,结合前人研究指出舰船目标检测数据集的发展应关注分类粒度的适宜性、标注的一致性和数据集的易扩充性,应加强对多尺度目标(尤其是小型目标)检测的模型结构的研究,为进一步提升舰船目标检测任务的综合性能,促进舰船目标检测技术的应用提供了新的思路。     

背景重建在视频监视系统中的应用

利用FPGA和USB控制芯片实现了实时视频监视采集系统,根据视频监视和传输特点,将采集得到的图像数据先做背景重建,然后利用重建得到的背景进行视频对象分割,最后将重建的背景、视频对象以及视频对象在视频图像中的位置保存,作为后续视频检测分析的依据.实验结果表明本文给出的背景重建算法能够较好地重建背景,得到较精确的运动目标,同时能够大幅度的提高实时视频对象分割速率.     

背景重建在视频监视系统中的应用

利用FPGA和USB控制芯片实现了实时视频监视采集系统,根据视频监视和传输特点,将采集得到的图像数据先做背景重建,然后利用重建得到的背景进行视频对象分割,最后将重建的背景、视频对象以及视频对象在视频图像中的位置保存,作为后续视频检测分析的依据。实验结果表明本文给出的背景重建算法能够较好地重建背景,得到较精确的运动目标,同时能够大幅度的提高实时视频对象分割速率。     

Image segmentation using a multilayer level-set approach

We propose an efficient multilayer segmentation method based on implicit curve evolution and on variational approach. The proposed formulation uses the minimal partition problem as formulated by D. Mumford and J. Shah, and can be seen as a more efficient extension of the segmentation models previously proposed in Chan and Vese (Scale-Space Theories in Computer Vision, Lecture Notes in Computer Science, Vol. 1682, pp. 141–151, 1999, IEEE Trans Image Process 10(2):266–277, 2001), and Vese and Chan (Int J Comput Vis 50(3):271–293, 2002). The set of unknown discontinuities is represented implicitly by several nested level lines of the same function, as inspired from prior work on island dynamics for epitaxial growth (Caflisch et al. in Appl Math Lett 12(4):13, 1999; Chen et al. in J Comput Phys 167:475, 2001). We present the Euler–Lagrange equations of the proposed minimizations together with theoretical results of energy decrease, existence of minimizers and approximations. We also discuss the choice of the curve regularization and conclude with several experimental results and comparisons for piecewise-constant segmentation of gray-level and color images.     

面向实时交通视觉监控的综合动态背景更新方法

为了从复杂的交通场景中获取高质量的背景图像，提出了一种综合的动态背景更新方法。同时使用了帧差信息和提取的高层对象状态信息对混合高斯背景模型进行选择性更新，克服了因较长时间停车对背景的影响，并能及时消除由于背景物体移出造成的鬼影。对实时交通视频的处理效果表明，该方法兼具良好的自适应性与鲁棒性。     

High dynamic range image deghosting by fast approximate background modelling

High Dynamic Range (HDR) images of real world scenes often suffer from ghosting artifacts caused by motion in the scene. Existing solutions to this problem typically either only address specific types of ghosting, or are very computationally expensive.We address ghosting by performing change detection on exposure-normalized images, then reducing the contribution of moving objects to the final composite on a frame-by-frame basis. Change detection is computationally advantageous and it can be applied to images exhibiting varied ghosting artifacts. We demonstrate our method both for Low Dynamic Range (LDR) and HDR images. Additional constraints based on a priori knowledge of the changing exposures apply to HDR images. We increase the stability of our approach by using recent superpixel segmentation techniques to enhance the change detection. Our solution includes a novel approach for areas that see motion throughout the capture, e.g., foliage blowing in the wind.We demonstrate the success of our approach on challenging ghosting scenarios, and that our results are comparable to existing state-of- the-art methods, while providing computational savings over these methods.     

基于颜色的快速人体跟踪及遮挡处理

为了对被跟踪到的运动员进行运动姿态以及运动参数的分析,给运动员的训练提供科学合理的参考,提高比赛成绩,研究了面向体育视频的运动目标跟踪技术,提出了一种基于Mean Shift的综合算法.首先,根据背景加权直方图选择跟踪目标与背景图像的差别最显著的部分作为跟踪特征,以减少背景信息对跟踪效果的影响;其次,针对Mean Shift算法需要对图像进行穷举匹配的问题,利用Kalman滤波对目标的状态进行有效预测,减少了匹配运算次数,改善了快速运动目标的跟踪效果,提高了跟踪算法的稳健性;最后运用基于核的Mean Shift算法对运动目标进行跟踪,同时进行目标模板的实时更新,实现了对体育视频中运动员的稳定实时的跟踪.该方法成功地解决了部分遮挡、背景混乱以及目标尺寸变化等问题.     

Fast background subtraction using static and dynamic gates

Background subtraction is usually one of the first steps carried out in motion detection using static video cameras. This paper presents a new fast model for background subtraction that processes only some pixels of each image. This model achieves a significant reduction in computation time that can be used for subsequent image analysis. Some regions of interest (ROI) are located where movement can start. If no movement is present in the image, only pixels of these ROIs are processed. Once a moving object is detected, a new ROI that follows it is created. Thus, motion detection and parameter updates are executed only in the relevant areas instead of in the whole image. The proposed model has three main advantages: the computational time can be reduced drastically, motion detection performance is improved, and it can be combined with most of the existing background subtraction techniques. These features make it specially suitable for security applications.     

动态模式的快速运动估计算法

针对H.264视频编码标准中运动估计的高计算复杂度,提出了一种动态模式的快速运动估计算法。该算法通过判断宏块的运动大小及运动方向选择相应的搜索模式;同时对标准中的中值预测进行了改进并提出了一种动态的参考块提前跳过策略。实验结果表明,该算法在保持良好的率失真性能的基础上,减少了运动估计时间,相对于快速全搜索算法FFS以及UMHexagonS算法,该算法分别减少了85.28%和35.29%的运动估计时间。     

Tracking dynamic textures using a particle filter driven by intrinsic motion information

In this paper, a new method for tracking dynamic textures is presented. Its novelty is to use a particle filter driven by the intrinsic motion of the tracked dynamic texture. Many research works have indeed shown that dynamic textures are well characterized by their intrinsic motion (in proceedings of 4th international conference on computer recognition systems CORES’05, pp. 17–26, 2005). In this work, we compute motion statistics of dynamic textures and use them in the observation model of our particle filter. Our tracking method is successfully applied on test sequences. The algorithm is fast and is able to track a dynamic texture moving on another dynamic texture with different intrinsic dynamics. The method is also able to track a dynamic texture in cases where classical particle filters based on color information only fail. Comments and future prospects raised by this method are finally described.     

Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images

Automatic liver segmentation is difficult because of the wide range of human variations in the shapes of the liver. In addition, nearby organs and tissues have similar intensity distributions to the liver, making the liver's boundaries ambiguous. In this study, we propose a fast and accurate liver segmentation method from contrast-enhanced computed tomography (CT) images. We apply the two-step seeded region growing (SRG) onto level-set speed images to define an approximate initial liver boundary. The first SRG efficiently divides a CT image into a set of discrete objects based on the gradient information and connectivity. The second SRG detects the objects belonging to the liver based on a 2.5-dimensional shape propagation, which models the segmented liver boundary of the slice immediately above or below the current slice by points being narrow-band, or local maxima of distance from the boundary. With such optimal estimation of the initial liver boundary, our method decreases the computation time by minimizing level-set propagation, which converges at the optimal position within a fixed iteration number. We utilize level-set speed images that have been generally used for level-set propagation to detect the initial liver boundary with the additional help of computationally inexpensive steps, which improves computational efficiency. Finally, a rolling ball algorithm is applied to refine the liver boundary more accurately. Our method was validated on 20 sets of abdominal CT scans and the results were compared with the manually segmented result. The average absolute volume error was 1.25+/-0.70%. The average processing time for segmenting one slice was 3.35 s, which is over 15 times faster than manual segmentation or the previously proposed technique. Our method could be used for liver transplantation planning, which requires a fast and accurate measurement of liver volume.     

Stress-constrained topology optimization: a topological level-set approach

The objective of this paper is to introduce and demonstrate an algorithm for stress-constrained topology optimization. The algorithm relies on tracking a level-set defined via the topological derivative. The primary advantages of the proposed method are: (1) the stresses are well-defined at all points within the evolving topology, (2) the finite-element stiffness matrices are well-conditioned, making the analysis robust and efficient, (3) the level-set is tracked through a simple iterative process, and (4) the stress constraint is precisely satisfied at termination. The proposed algorithm is illustrated through numerical experiments in 2D and 3D.     

Topology optimization of shell-infill structures using a distance regularized parametric level-set method

This paper presents a novel topology optimization formulation for shell-infill structures based on a distance regularized parametric level-set method (PLSM). In this method, the outer shell and the infill are represented by two distinct level sets of a single-level set function (LSF). In order to obtain a controllable and uniform shell thickness, a distance regularization (DR) term is introduced to formulate a weighted bi-objective function. The DR term is minimized along with the original objective, regularizing the parametric LSF close to a signed distance function. With the signed distance property, the area between the two-level sets can be contoured as the shell with a uniform thickness. Additionally, the presented formulation retains one important merit of the PLSM that new holes are able to nucleate during the optimization process. With respect to the material of the shell, the infill is filled with a weaker and lighter material with tunable parameters. Particularly, the infill can be pre-designed with isotropic microstructures. Three compliance minimization examples are provided to demonstrate the effectiveness of this formulation.

     

不确定混沌系统的动态神经网络跟踪控制

针对不确定非线性混沌系统，提出了一种基于动态神经网络辨识器的自适应跟踪控制新方法，通过滑模控制技术在线调整动态神经网络辨识器权值，并在获取动态神经网络模型的基础上设计出优化控制器，实现混沌系统的轨道跟踪，对辨识误差和轨道跟踪误差进行分析并证明了它们的有界性，Lorenz混沌系统的仿真实验结果表明了控制策略的有效性。     

Review of background subtraction methods using Gaussian mixture model for video surveillance systems

Foreground detection or moving object detection is a fundamental and critical task in video surveillance systems. Background subtraction using Gaussian Mixture Model (GMM) is a widely used approach for foreground detection. Many improvements have been proposed over the original GMM developed by Stauffer and Grimson (IEEE Computer Society conference on computer vision and pattern recognition, vol 2, Los Alamitos, pp 246–252, 1999. doi: 10.1109/CVPR.1999.784637) to accommodate various challenges experienced in video surveillance systems. This paper presents a review of various background subtraction algorithms based on GMM and compares them on the basis of quantitative evaluation metrics. Their performance analysis is also presented to determine the most appropriate background subtraction algorithm for the specific application or scenario of video surveillance systems.     

A traffic surveillance system using dynamic saliency map and SVM boosting

This paper proposes a traffic surveillance system that can efficiently detect an interesting object and identify vehicles and pedestrians in real traffic situations. The proposed system consists of a moving object detection model and an object identification model. A dynamic saliency map is used for analyzing dynamics of the successive static saliency maps, and can localize an attention area in dynamic scenes to focus on a specific moving object for traffic surveillance purposes. The candidate local areas of a moving object are followed by a blob detection processing including binarization, morphological closing and labeling methods. For identifying a moving object class, the proposed system uses a hybrid of global and local information in each local area. Although the global feature analysis is a compact way to identify an object and provide a good accuracy for non-occluded objects, it is sensitive to image translation and occlusion. Therefore, a local feature analysis is also considered and combined with the global feature analysis. In order to construct an efficient classifier using the global and local features, this study proposes a novel classifier based on boosting of support vector machines. The proposed object identification model can identify a class of moving object and discard unexpected candidate area which does not include an interesting object. As a result, the proposed road surveillance system is able to detect a moving object and identify the class of the moving object. Experimental results show that the proposed traffic surveillance system can successfully detect specific moving objects.     

ALGAMES: a fast augmented Lagrangian solver for constrained dynamic games

Autonomous Robots - Dynamic games are an effective paradigm for dealing with the control of multiple interacting actors. This paper introduces augmented Lagrangian GAME-theoretic solver (ALGAMES),...     

Area bound dynamic time warping based fast and accurate person authentication using a biometric pen

The paper presents a modified dynamic time warping (DTW) technique for person authentication based on time series matching obtained from handwriting. The online data has been acquired by a biometric smart pen device. The proposed method allows fast and accurate classification of human individuals based on handwritten PIN words or signature samples. Although classic DTW provides robust distance measurements essential for accurate classification of sequences, it is computationally expensive. To speed up computations we introduce area bound dynamic time warping (AB_DTW) that divides time series into several areas bounded by segments of consecutive zero crossings including local peaks and valleys. Unlike classic DTW which compares whole signals, the proposed AB_DTW warps areas bounded by the local regions. Two kinds of data abstraction formats of area bound—1 dimensional and 2 dimensional—are evaluated. Experimental results show that because of a higher-level data abstraction, the proposed approach is several times faster than classic DTW. Moreover, AB_DTW does not offer substantial loss of accuracy which is required for authentication performance using handwritten PIN words and signatures sampled by biometric pen device.