ВІДСЛІДКОВУВАННЯ РУХОМИХ ОБ’ЄКТІВ У ВІДЕОПОТОКАХ РЕАЛЬНОГО ЧАСУ

This article describes a new method for tracking moving objects in the field of multiple cameras. The main feature of the proposed method is the ability to work in real time by significantly reducing procedural complexity. Based on this method, authors developed system to identify and support transport traffic. The results of practical experiments on the system show high accuracy of identification of moving objects, building the exact trajectory of their movement and possibility of them accompanied. Numerous practical experiments confirmed the efficiency of the proposed method in video surveillance systems with up to 8 cameras.Описано новий метод відслідковування рухомих об’єктів в полі зору декількох камер відеоспостереження. Основною особливістю розробленого методу є можливість роботи в режимі реального часу завдяки значному зменшенню процедурної складності. На основі розробленого методу створено систему багатокамерної ідентифікації та супроводу руху транспортних засобів. Результати практичних експериментів щодо роботи системи показують високу точність ідентифікації рухомих об’єктів, побудову точної траєкторії їх руху та можливість їх супроводу. Численні практичні експерименти підтвердили ефективність використання розробленого методу у системах відеоспостереження, що містять до 8 камер

Object Detection and Tracking Based on Visual Saliency

基于视频的目标检测与跟踪是计算机视觉领域热点的研究方向之一，它在智能视频监控、军事侦察监视、交通管理和无人驾驶等领域有着广泛的应用，并发挥着举足轻重的作用。 在机器视觉中，一般的视频跟踪技术需要在第一帧手动地标记出运动目标。本文针对这一问题，研究如何让机器自动发现显著物并进行跟踪：利用视觉显著性对目标进行检测，通过词袋模型形成对运动目标的观测，结合粒子滤波跟踪算法对运动目标进行跟踪。主要的研究工作及创新点如下： 1.提出一种基于多线索视觉显著性融合的运动目标检测算法。利用中央周边差异显著性来检测局部对比度强的显著区域，利用谱残差显著性检测图像在空间域上的显著区域，利用动态显著性来检测具有运...Video-based target detection and tracking is one of the research hotspots in the field of computer vision. It plays a very important role in many applications, such as smart surveillance, military reconnaissance and surveillance, traffic management and auto driving. In machine vision, tracking always needs to label the object by human on the first frame. According to this problem, this thesis res...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302007115126

Signal-to-Noise Behavior for Matches to Gradient Direction Models of Corners in Images

Gradient direction models for corners of prescribed acuteness, leg length, and leg thickness are constructed by generating fields of unit vectors emanating from leg pixels that point normal to the edges. A novel FFT-based algorithm that quickly matches models of corners at all possible positions and orientations in the image to fields of gradient directions for image pixels is described. The signal strength of a corner is discussed in terms of the number of pixels along the edges of a corner in an image, while noise is characterized by the coherence of gradient directions along those edges. The detection-false alarm rate behavior of our corner detector is evaluated empirically by manually constructing maps of corner locations in typical overhead images, and then generating different ROC curves for matches to models of corners with different leg lengths and thicknesses. We then demonstrate how corners found with our detector can be used to quickly and automatically find families of polygons of arbitrary position, size and orientation in overhead images

Simulation of biologically inspired object movement for the study of object tracking algorithms

Major advances in Cell and Molecular Biology have been associated with the advances in live-cell microscopy imaging, and these studies started to rely on temporal single cell imaging. To support these efforts, available automated image analysis methods such as cell segmentation and cell tracking during a time-series analysis should be improved. One important step is the validation of such image processing methods. Ideally, the “ground truth” should be known, which is possible only by manually labelling images or by artificially produced images. To simulate such artificial images we developed a platform that can simulate biologically inspired objects, by generating bodies with different morphologies, physical movement and that can aggregate in clusters. Using this platform, we tested and compared four tracking algorithms: Simple Nearest-Neighbour (NN), NN with Morphology and two DBSCAN based ones. In this work we showed that Simple NN work for small object velocities, while the other algorithms perform better on higher velocities and when clustered. This platform can generate new benchmark images and is openly available to test other tracking algorithms. (http://griduni.uninova.pt/Clustergen/ClusterGen_v1.0.zip

Tracking features in image sequences with Kalman filtering, global optimization, mahalanobis distance and a management model

This work addresses the problem of tracking feature points along image sequences. In order to analyze the undergoing movement, an approach based on the Kalman filtering technique has been used, which basically carries out the estimation and correction of the features' movement in every image frame. So as to integrate the measurements obtained from each image into the Kalman filter, a data optimization process has been adopted to achieve the best global correspondence set. The proposed criterion minimizes the cost of global matching, which is based on the Mahalanobis distance. A management model is employed to manage the features being tracked. This model adequately deals with problems related to the occlusion of the tracked features, the appearance of new features, as well as optimizing the computational resources used. Experimental results obtained through the use of the proposed tracking framework are presented

Initial steps towards a multilevel functional principal components analysis model of dynamical shape changes

In this article, multilevel principal components analysis (mPCA) is used to treat dynamical changes in shape. Results of standard (single-level) PCA are also presented here as a comparison. Monte Carlo (MC) simulation is used to create univariate data (i.e., a single “outcome” variable) that contain two distinct classes of trajectory with time. MC simulation is also used to create multivariate data of sixteen 2D points that (broadly) represent an eye; these data also have two distinct classes of trajectory (an eye blinking and an eye widening in surprise). This is followed by an application of mPCA and single-level PCA to “real” data consisting of twelve 3D landmarks outlining the mouth that are tracked over all phases of a smile. By consideration of eigenvalues, results for the MC datasets find correctly that variation due to differences in groups between the two classes of trajectories are larger than variation within each group. In both cases, differences in standardized component scores between the two groups are observed as expected. Modes of variation are shown to model the univariate MC data correctly, and good model fits are found for both the “blinking” and “surprised” trajectories for the MC “eye” data. Results for the “smile” data show that the smile trajectory is modelled correctly; that is, the corners of the mouth are drawn backwards and wider during a smile. Furthermore, the first mode of variation at level 1 of the mPCA model shows only subtle and minor changes in mouth shape due to sex; whereas the first mode of variation at level 2 of the mPCA model governs whether the mouth is upturned or downturned. These results are all an excellent test of mPCA, showing that mPCA presents a viable method of modeling dynamical changes in shape

Image Processing and Simulation Toolboxes of Microscopy Images of Bacterial Cells

Recent advances in microscopy imaging technology have allowed the characterization of the dynamics of cellular processes at the single-cell and single-molecule level. Particularly in bacterial cell studies, and using the E. coli as a case study, these techniques have been used to detect and track internal cell structures such as the Nucleoid and the Cell Wall and fluorescently tagged molecular aggregates such as FtsZ proteins, Min system proteins, inclusion bodies and all the different types of RNA molecules. These studies have been performed with using multi-modal, multi-process, time-lapse microscopy, producing both morphological and functional images. To facilitate the finding of relationships between cellular processes, from small-scale, such as gene expression, to large-scale, such as cell division, an image processing toolbox was implemented with several automatic and/or manual features such as, cell segmentation and tracking, intra-modal and intra-modal image registration, as well as the detection, counting and characterization of several cellular components. Two segmentation algorithms of cellular component were implemented, the first one based on the Gaussian Distribution and the second based on Thresholding and morphological structuring functions. These algorithms were used to perform the segmentation of Nucleoids and to identify the different stages of FtsZ Ring formation (allied with the use of machine learning algorithms), which allowed to understand how the temperature influences the physical properties of the Nucleoid and correlated those properties with the exclusion of protein aggregates from the center of the cell. Another study used the segmentation algorithms to study how the temperature affects the formation of the FtsZ Ring. The validation of the developed image processing methods and techniques has been based on benchmark databases manually produced and curated by experts. When dealing with thousands of cells and hundreds of images, these manually generated datasets can become the biggest cost in a research project. To expedite these studies in terms of time and lower the cost of the manual labour, an image simulation was implemented to generate realistic artificial images. The proposed image simulation toolbox can generate biologically inspired objects that mimic the spatial and temporal organization of bacterial cells and their processes, such as cell growth and division and cell motility, and cell morphology (shape, size and cluster organization). The image simulation toolbox was shown to be useful in the validation of three cell tracking algorithms: Simple Nearest-Neighbour, Nearest-Neighbour with Morphology and DBSCAN cluster identification algorithm. It was shown that the Simple Nearest-Neighbour still performed with great reliability when simulating objects with small velocities, while the other algorithms performed better for higher velocities and when there were larger clusters present