A Comparative Analysis Using Silhouette Extraction Methods for Dynamic Objects in Monocular Vision

Moving or dynamic object analysis continues to be an increasingly active research field in computer vision with many types of research investigating different methods for motion tracking, object recognition, pose estimation, or motion evaluation (e.g. in sports sciences). Many techniques are available to measure the forces and motion of the people, such as force plates to measure ground reaction forces for a jump or running sports. In training and commercial solution, the detailed motion of athlete's available motion capture devices based on optical markers on the athlete's body and multiple calibrated fixed cameras around the sides of the capture volume can be used. In some situations, it is not practical to attach any kind of marker or transducer to the athletes or the existing machinery are being used, while it is required by a pure vision-based approach to use the natural appearance of the person or object. When a sporting event is taking place, there are opportunities for computer vision to help the referee and other personnel involved in the sports to keep track of incidents occurring, which may provide full coverage and analysis in details of the event for sports viewers. The research aims at using computer vision methods, specially designed for monocular recording, for measuring sports activities, such as high jump, wide jump, or running. Just for indicating the complexity of the project: a single camera needs to understand the height at a particular distance using silhouette extraction. Moving object analysis benefits from silhouette extraction and this has been applied to many domains including sports activities. This paper comparatively discusses two significant techniques to extract silhouettes of a moving object (a jumping person) in monocular video data in different scenarios. The results show that the performance of silhouette extraction varies in dependency on the quality of used video data.

Virtual interaction algorithm of cultural heritage based on multi feature fusion

During the traditional cultural heritage virtual interaction algorithm in the interaction action recognition, the database is too single, resulting in low recognition accuracy, recognition time-consumer and other issues. Therefore, this paper introduces the multi feature fusion method to optimize the cultural heritage virtual interaction algorithm. Kinect bone tracking technology is applied to identify the movement of the tracking object, 20 joints of the human body are tracked, and interactive action recognition is realized according to the fingertip candidate points. In order to carry out the judgment virtual interactive operation of subsequent recognition actions, a multi feature fusion database is established. The mean shift is used to derive the moving mean of the target’s action position and to track the interactive object. The Euclidean distance formula is used to train samples of multi feature fusion database data to realize the judgment of recognition action and virtual interaction. In order to verify the feasibility of the research algorithm, the virtual interactive script of ink painting in a cultural heritage museum is used to simulate the research algorithm, and a comparative experiment is designed. The experimental results show that the proposed algorithm is superior to the traditional virtual interactive algorithm in recognition accuracy and efficiency, which proves the feasibility of this method.

OBSERVER’S TRAJECTORY TRACKING OBJECT BYPASSING OBSTACLE ON THE SHORTEST CURVE

Motion of some object is considered. The object t moves from the initial point t∗ to the final one t ∗ . But since absent of the direct path, he should bypass an obstacle a connected bodily set G. It is supposed that t moves by the most short trajectory T = Tt , and the trajectory T is a convex curve. The observer’s f task is to find the trajectory Tf that provides tracking the object on the most part of the object’s motion and, if possible, the lesser object’s stealth of motion along the trajectory T . The latency is defined by the distance that the observer must pass to see the object in the field of vision. The object and observer start at the same initial instant, and their velocities are equal. In the paper, examples of the trajectories Tf in R 2 are presented, on which the observer can see the object’s trajectory T ; also, the value of the object’s latency is shown for the invisible parts of the trajectory T . The variant of Tf in R 3 is shown.

ANALISIS APLIKASI AUGMENTED REALITY UNTUK KATALOG JAM TANGAN BERBASIS ANDROID

Dengan perkembangan teknologi saat ini, komputer telah membawa banyak manfaat bagi umat manusia dalam berbagai bidang informasi, pendidikan, komunikasi, serta bisnis. Dalam bisnis ada berbagai cara bisnis yang dilakukan para pemilik toko maupun supermarket. Katalog merupakan suatu lembaran daftar produk yang dimiliki setiap toko atau supermarket, katalog digunakan untuk menarik minat pembeli serta memilih produk yang di inginkan sesuai dengan daftar di katalog sebelum membeli produk tersebut. Penelitian ini bertujuan untuk membuat aplikasi bisnis yang menggunakan teknologi sebagai media alternatif untuk menampilkan objek virtual berpadu dengan media katalog. Aplikasi ini memanfaatkan teknologi Augmented Reality (AR) dengan menggunakan metode Marker Based Tracking Object. Penelitian ini menjadi salah satu opsi ide bisnis untuk menarik perhatian serta minat para pembeli dalam memilih berbagai produk jam tangan yang ada di katalog.Kata Kunci: Katalog, Fashion, Augmented Reality (AR), Marker Based Tracking Object

An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on publicly common datasets that the performance of the proposed framework is superior to representative state-of-the-art methods.

Analysis of visual object tracking algorithms for real-time systems

The article analyzes the latest achievements and decisions in the process of visual support of the target object in the field of computer vision, considers approaches to the choice of algorithm for visual support of objects on video sequences, highlights the main visual features that can be based on tracking object. The criteria that influence the choice of the target object-tracking algorithm in real time are defined. However, for real-time tracking with limited computing resources, the choice of the appropriate algorithm is crucial. The choice of visual tracking algorithm is also influenced by the requirements and limitations for the monitored objects and prior knowledge or assumptions about them. As a result of the analysis, the Staple tracking algorithm was preferred, according to the criterion of speed, which is a crucial indicator in the design and development of software and hardware for automated visual support of the object in real-time video stream for various surveillance and security systems, monitoring traffic, activity recognition and other embedded systems.

Estimation for Motion in Tracking and Detection Objects with Kalman Filter

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

A Study on the Active Vector Model for Object Tracking

In this thesis we propose a new active vector model which is able to track objects with an algorithm. First of all we classified a few basic shapes as the modes of the tracking object, which were learned by the principle components analysis, and then we extracted the representative feature vector and the minimum shape parameters. And we reorganize the sequence of basic shape change to the shape change based on the feature point vector. We modeled the object of target tracking and its moving using both the feature position vector and shape change vector obtained by the above process. The proposed method generates parameterized values based on the moving pattern of the object, provides better stability of the local structure than other models, and decreases the cost of convergence duration, which is the weakness of model-based tracking algorithms.

Literatur Riview Transliterasi Hurub Arab ke Latin

Computer vision adalah bidang interdisiplin yang mempelajari tentang bagaimana komputer dapat melakukan pemahaman terhadap citra digital dan video. Dalam persepektif engineering computer vision ditujukan untuk melakukan automasi terhadap sistem visual manusia. Tahap dalam computer vision meliputi pengambilan (acquiring), pemrosesan (processing), analisis (analyzing) dan pemahaman (understanding) terhadap citra digital. Computer vision berfokus pada sistem cerdas yang dapat melakukan ekstraksi data dari citra digital ke dalam bentuk numerik Sub domain dari computer vision meliputi scene reconstruction, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, dan image restoration. Handwriting character recognition (pengenalan tulisan tangan) adalah salah satu cabang dari object recognition, yaitu kemampuan komputer untuk menerima dan menafsirkan input tulisan tangan yang dapat dimengerti dari sumber seperti dokumen kertas, foto, layar sentuh dan perangkat lainnya. Gambar dari teks tertulis dapat digunakan secara luring dari selembar kertas oleh pemindai optik (rekognisi karakter optik). Selain itu, gerakan ujung pena dapat dimengerti secara daring, misalnya dengan menggunakan permukaan layar komputer berbasis pena. Salah satu aksara yang dijadikan objek dalam pengenalan tulisan tangan adalah huruf huruf arab pegon (yang selanjutnya disebut pegon). Huruf pegon biasa digunakan dalam terjemah kitab kuning dalam bahasa daerah (umunya) Jawa, Sunda, atau Melayu. Kenyataannya terjemah kitab-kitab kuning klasik di indonesian lebih banyak menggunakan 3 bahasa tersebut, sehingga orang-orang yang tidak menguasai salah satu dari bahasa tersebut akan kesuliatan untuk mendapatkan terjemah.Berbeda dengan huruf arab baku, huruf pegon memiliki beberapa karakter yang merupakan rekayasa agar dapat dibaca menyesuaikan lidah bahasa daerah bersangkutan. Masalah yang muncul adalah huruf pegon tidak dapat dibaca oleh seseorang yang tidak memiliki kosa kata dalam bahasa bersangkutan sehingga membutuhkan proses penerjemahan. Proses penerjemahan sendiri hanya mungkin dilakukan jika kalimat tertulis dalam huruf latin. Pengenalan tulisan tangan saat ini sudah banyak dilakukan menggunakan banyak metode, terutama yang paling banyak dari varian Jaringan Syaraf Tiruan (neural network). Walaupun tingkat akurasi dari neural network tinggi, tetapi computatiion cost metode-metode ini sangat besar. Eigenspace adalah subspace dari aljabar linier yang terdiri dari sekumpulan eigen vector. Tiap eigen vector terbentuk dari banyak eigen value. Salah satu pemanfaatannya adalah eigenface yang digunakan dalam pengenalan wajah. Caranya adalah dengan mengubah citra digital ke dalam eigen value yang kemudian disusun menjadi eigen vector. Data uji kemudian akan dihitung jaraknya terhadap semua vector yang ada kemudian diambil yang nilainya paling dekat. Metode ini cukup low cost. Keyword: transliterasi, eigen, huruf arab, huruf latin