The Representation of Image Texture

This thesis explores how to represent image texture in order to obtain information about the geometry and structure of surfaces, with particular emphasis on locating surface discontinuities. Theoretical and psychophysical results lead to the following conclusions for the representation of image texture: (1) A texture edge primitive is needed to identify texture change contours, which are formed by an abrupt change in the 2-D organization of similar items in an image. The texture edge can be used for locating discontinuities in surface structure and surface geometry and for establishing motion correspondence. (2) Abrupt changes in attributes that vary with changing surface geometry ??ientation, density, length, and width ??ould be used to identify discontinuities in surface geometry and surface structure. (3) Texture tokens are needed to separate the effects of different physical processes operating on a surface. They represent the local structure of the image texture. Their spatial variation can be used in the detection of texture discontinuities and texture gradients, and their temporal variation may be used for establishing motion correspondence. What precisely constitutes the texture tokens is unknown; it appears, however, that the intensity changes alone will not suffice, but local groupings of them may. (4) The above primitives need to be assigned rapidly over a large range in an image

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

СУПРОВІД РУХОМИХ ОБ’ЄКТІВ У ЛОКАЛЬНИХ СИСТЕМАХ ВІДЕОСПОСТЕРЕЖЕННЯ МІСЬКОЇ ІНФРАСТРУКТУРИ

The tasks of method development for construction of computing systems of moving objects automatized tracking in videostreams in real time are shown. The methods of background and foreground objects generation and tracking of moving objects have been developed to complete the task. The methodology of foreground model generation is based on using the mixture of Gaussian distributions and morphological operations. And methodology of moving objects tracking is based on model of motion. The results of practical experiments showed efficiency of developed system. Trajectories and trajectory parameters estimation are quite precise and in practical realization meet the realtime criteria.Розглядається задача розроблення методу для побудови комп’ютеризованих систем автоматизованого відслідковування рухомих об’єктів у відеопотоках у реальному часі. Для розв’язання задачі загалом розроблено методику генерування моделі фону та передньопланових об’єктів і супроводу рухомих об’єктів. Методика генерування моделі фону базується на використанні суміші Гаусових розподілів та мофологічних операцій. А методика супроводу – на моделі руху.Результати практичних експериментів засвідчили ефективність розробленої системи. Визначення траєкторії та параметрів руху є високим і у практичній реалізації задовольняє умови реального часу

Evaluation of collision properties of spheres using high-speed video analysis

Experimental evaluation of the collision properties of spheres is performed using video image analysis techniques. A high-speed Kodak EktaPro1000 video camera is utilized to record a collision sequence between two spheres at 1000 frames/sec, and then the images are analyzed to calculate three dimensional translation and rotation before and after the collision. These quantities are used to compute the collision properties for a pair of one inch nylon spheres, i.e. the coefficient of friction, and the coefficients of normal and tangential restitution. The focus of the thesis is on image analysis techniques that provide high accuracy results even though the image resolution is very low, i.e. 240x192 pixels. The procedure developed here can be extended to smaller size spheres and can also be applicable to other motion analysis expenments involving low resolution images

Local search heuristics for the multidimensional assignment problem

The Multidimensional Assignment Problem (MAP) (abbreviated s-AP in the case of s dimensions) is an extension of the well-known assignment problem. The most studied case of MAP is 3-AP, though the problems with larger values of s also have a large number of applications. We consider several known neighborhoods, generalize them and propose some new ones. The heuristics are evaluated both theoretically and experimentally and dominating algorithms are selected. We also demonstrate that a combination of two neighborhoods may yield a heuristics which is superior to both of its components

Anomaly activity classification in the grocery stores

Nowadays, because of the growing number of robberies in shopping malls and grocery stores, automatic camera’s applications are vital necessities to detect anomalous actions. These events usually happen quickly and unexpectedly. Therefore, having a robust system which can classify anomalies in a real-time with minimum false alarms is required. Due to this needs, the main objective of this project is to classify anomalies which may happen in grocery stores. This objective is acquired by considering properties, such as; using one fixed camera in the store and the presence of at least one person in the camera view. The actions of human upper body are used to determine the anomalies. Articulated motion model is used as the basis of the anomalies classification design. In the design, the process starts with feature extraction and followed by target model establishment, tracking and action classification. The features such as color and image gradient built the template as the target model. Then, the models of different upper body parts are tracked during consecutive frames by the tracking method which is sum of square differences (SSD) combined with the Kalman filter as the predictor. The spatio-temporal information as the trajectory of limbs gained by tracking part is sent to proposed classification part. For classification, three different scenarios are studied: attacking cash machine, cashier’s attacking and making the store messy. In implementing these scenarios, some events were introduced. These events are; basic (static) events which are the static objects in the scene, spatial events which are those actions depend on coordinates of body parts and spatio-temporal events in which these actions are tracked in consecutive frames. At last, if one of the scenarios happens, an anomalous action will be detected. The results show the robustness of the proposed methods which have the minimum false positive error of 7% for the cash machine attack and minimum false negative error of 19% for the cashier’s attacking scenario

Point tracking: an a-contrario approach

In this work, we propose a new approach to recover trajectories from points previously detected in a sequence of images. In presence of spurious and missing detections, actual trajectories can be characterized by an a-contrario model, that estimates the probability of observing a similar trajectory in random data. This results in a single criterion combining trajectory characteristics (duration, number of points, smoothness) and data statistics (number of images and detected points), which can then be used to drive a dynamic programming algorithm able to extract sequentially the most meaningful trajectories. The performances obtained on synthetic and real-world data are studied in detail, and shown to compare favorably to the state-of-the-art ROADS algorithm

A java framework for object detection and tracking, 2007

Object detection and tracking is an important problem in the automated analysis of video. There have been numerous approaches and technological advances for object detection and tracking in the video analysis. As one of the most challenging and active research areas, more algorithms will be proposed in the future. As a consequence, there will be the demand for the capability to provide a system that can effectively collect, organize, group, document and implement these approaches. The purpose of this thesis is to develop one uniform object detection and tracking framework, capable of detecting and tracking the multi-objects in the presence of occlusion. The object detection and tracking algorithms are classified into different categories and incorporated into the framework implemented in Java. The framework can adapt to different types, and different application domains, and be easy and convenient for developers to reuse. It also provides comprehensive descriptions of representative methods in each category and some examples to aspire to give developers or users, who require a tracker for a certain application, the ability to select the most suitable tracking algorithm for their particular needs