Калмановская фильтрация одного класса изображений динамических объектов

We discuss the problem of estimating the state of a dynamic object by using observed images generated by an optical system. The work aims to implement a novel approach that would ensure improved accuracy of dynamic object tracking using a sequence of images. We utilize a vector model that describes the object image as a limited number of vertexes (reference points). Upon imaging, the object of interest is assumed to be retained at the center of each frame, so that the motion parameters can be considered as projections onto the axes of a coordinate system matched with the camera's optical axis. The novelty of the approach is that the observed parameters (the distance along the optical axis and angular attitude) of the object are calculated using the coordinates of specified points in the object images. For estimating the object condition, a Kalman-Bucy filter is constructed on the assumption that the dynamic object motion is described by a set of equations for the translational motion of the center of mass along the optical axis and variations in the angular attitude relative to the image plane. The efficiency of the proposed method is illustrated by an example of estimating the object's angular attitude.Рассматривается задача оценивания состояния динамического объекта по наблюдаемым изображениям, сформированным оптической системой. Цель исследования состоит в реализации нового подхода, обеспечивающего повышение точности автономного слежения за динамическим объектом по последовательности изображений. Используется векторная модель изображения объекта в виде ограниченного количества вершин (базовых точек). Предполагается, что в процессе регистрации объект удерживается в центральной области каждого кадра, поэтому параметры движения могут описываться в виде проекций на оси системы координат, связанной с оптической осью камеры. Новизна подхода состоит в том, что наблюдаемые параметры (расстояние вдоль оптической оси и угловое положение) объекта вычисляются по координатам заданных точек на изображениях объекта. Для оценки состояний объекта строится фильтр Калмана-Бьюси в предположении, что движение динамического объекта описывается совокупностью уравнений поступательного движения центра масс вдоль оптической оси и изменений углового положения относительно плоскости изображения. Приведен пример оценивания углового положения объекта, иллюстрирующий работоспособность предложенного метода

An Alternative Vehicle Counting Tool Using the Kalman Filter within MATLAB

This study proposes an alternative and economical tool to estimate traffic densities, via video-image processing adapting the Kalman filter included in the Matlab code. Traffic information involves acquiring data for long periods of time at stationary points. Vehicle counting is vital in modern transport studies, and can be achieved by using different techniques, such as manual counts, use of pneumatic tubes, magnetic sensors, etc. In this research however, automatic vehicle detection was achieved using image processing, because it is an economical and sometimes even faster option. Commercial automatic vehicle detection and tracking programs/applications already exist, but their use is typically prohibitive due to their high cost. Large cities can obtain traffic recordings from surveillance cameras and process the information, but it is difficult for smaller towns without such infrastructure or even assigned budget. The proposed tool was developed taking into consideration these difficult situations, and it only requires users to have access to a fixed video camera placed at an elevated point (e.g. a pedestrian bridge or a light pole) and a computer with a powerful processor; the images are processed automatically through the Kalman filter code within Matlab. The Kalman filter predicts random signals, separates signals from random noise or detects signals with the presence of noise, minimizing the estimated error. It needs nevertheless some adjustments to focus it for vehicle counting. The proposed algorithm can thus be adapted to fit the users’ necessities and even the camera’s position. The use of this algorithm allows to obtain traffic data and may help small cities´ decision makers dealing with present and future urban planning and the design or installment of transportation systems

Determining Bus Stop Locations using Deep Learning and Time Filtering

This paper presents an intelligent bus stop determination from bus Global Positioning System (GPS) trajectories. A mixture of deep neural networks and a time filtering algorithm is used in the proposed algorithm. A deep neural network uses the speed histogram and azimuth angle at each location as input features. A deep neural networks consists of the convolutional neural networks (CNN), fully connected networks, and bidirectional Long-Short Term Memory (LSTM) networks. It predicts the soft decisions of bus stops at all locations along the route. The time filtering technique was adopted to refine the results obtained from the LSTM network. The time histograms of all locations was built where the high potential timestamps are extracted. Then, a linear regression is used to produce an approximate reliable timestamp. Each time distribution can be derived using data updated at that time slot and compared to a reference distribution. Locations are predicted as bus stop locations when timestamp distributions close to the reference distributions. Our technique was tested on real bus service GPS data from National Science and Technology Development Agency (NATDA, Thailand). The proposed method can outperform other existing bus stop detection systems

Vision-Based Automated Hole Assembly System with Quality Inspection

Automated manufacturing, driven by rising demands for mass-produced products, calls for efficient systems such as the peg-in-hole assembly. Traditional industrial robots perform these tasks but often fall short in speed during pick-and-place processes. This study presents an innovative mechatronic system for peg-in-hole assembly, integrating a novel peg insertion tool, assembly mechanism and control algorithm. This combination achieves peg insertion with a 200 µm tolerance without the need for pick-and-place, meeting the requirements for high precision and rapidity in modern manufacturing. Dual cameras and computer vision techniques, both traditional and machine learning (ML)-based, are employed to detect workpiece features essential for assembly. Traditional methods focus on image enhancement, edge detection and circular feature recognition, whereas ML verifies workpiece positions. This research also introduces a novel statistical quality inspection, offering an alternative to standard ML inspections. Through rigorous testing on varied workpiece surfaces, the robustness of the methods is affirmed. The assembly system demonstrates a 99.00% success rate, while the quality inspection method attains a 97.02% accuracy across diverse conditions, underscoring the potential of these techniques in automated assembly, defect detection and product quality assurance