Moving object detection for real-time augmented reality applications in a GPGPU

The last generation of consumer electronic devices is endowed with Augmented Reality (AR) tools. These tools require moving object detection strategies, which should be fast and efficient, to carry out higher level object analysis tasks. We propose a lightweight spatio-temporal-based non-parametric background-foreground modeling strategy in a General Purpose Graphics Processing Unit (GPGPU), which provides real-time high-quality results in a great variety of scenarios and is suitable for AR applications

Moving object detection strategy for augmented-reality applications in a GPGPU by using CUDA

A spatial-color-based non-parametric background-foreground modeling strategy in a GPGPU by using CUDA is proposed. This strategy is suitable for augmented-reality applications, providing real-time high-quality results in a great variety of scenarios

Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation.Comment: 4 pages, 6 figures, 4 tables; XIIth International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT 2017), Lviv, Ukrain

Region-based moving object detection using spatially conditioned nonparametric models in a GPU

A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections

On binaural spatialization and the use of GPGPU for audio processing

3D recordings and audio, namely techniques that aim to create the perception of sound sources placed anywhere in 3 dimensional space, are becoming an interesting resource for composers, live performances and augmented reality. This thesis focuses on binaural spatialization techniques. We will tackle the problem from three different perspectives. The first one is related to the implementation of an engine for audio convolution, this is a real implementation problem where we will confront with a number of already available systems trying to achieve better results in terms of performances. General Purpose computing on Graphic Processing Units (GPGPU) is a promising approach to problems where a high parallelization of tasks is desirable. In this thesis the GPGPU approach is applied to both offline and real-time convolution having in mind the spatialization of multiple sound sources which is one of the critical problems in the field. Comparisons between this approach and typical CPU implementations are presented as well as between FFT and time domain approaches. The second aspect is related to the implementation of an augmented reality system having in mind an “off the shelf” system available to most home computers without the need of specialized hardware. A system capable of detecting the position of the listener through a head-tracking system and rendering a 3D audio environment by binaural spatialization is presented. Head tracking is performed through face tracking algorithms that use a standard webcam, and the result is presented over headphones, like in other typical binaural applications. With this system users can choose audio files to play, provide virtual positions for sources in an Euclidean space, and then listen as if they are coming from that position. If users move their head, the signals provided by the system change accordingly in real-time, thus providing the realistic effect of a coherent scene. The last aspect covered by this work is within the field of psychoacoustic, long term research where we are interested in understanding how binaural audio and recordings are perceived and how then auralization systems can be efficiently designed. Considerations with regard to the quality and the realism of such sounds in the context of ASA (Auditory Scene Analysis) are propose

Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes

The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation Also, we present the results of testing neural networks architectures on H2O platform for various activation functions, stopping metrics, and other parameters of machine learning algorithm. It was demonstrated for the use case of MNIST database of handwritten digits in single-threaded mode that blind selection of these parameters can hugely increase (by 2-3 orders) the runtime without the significant increase of precision. This result can have crucial influence for optimization of available and new machine learning methods, especially for image recognition problems.Comment: 15 pages, 11 figures, 4 tables; this paper summarizes the activities which were started recently and described shortly in the previous conference presentations arXiv:1706.02248 and arXiv:1707.04940; it is accepted for Springer book series "Advances in Intelligent Systems and Computing

High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms

Essentials of Augmented Reality Software Development under Android Patform

Liitreaalsus on üha enam arenev tehnoloogia. Lisaks meelelahutuseleon liitreaalsus leidnud kasutust nii meditsiinis, sõjaväes, masinaehituses kui ka teistes suurtes ettevõtluse ning riigiga seotud valdkondades. Arendusmeeskondade eesmärk on saavutada võimalikult hea jõudlus ning visuaalsed tulemused nende poolt toodetavas tarkvaras sõltumata kasutuspiirkonnast. Liitreaalsuse tarkvara põhitehnoloogia sõltub vägapalju meeskonnale kättesaadavatest ressurssidest. See tähendab, et paremate võimalustega organisatsioonid saavad lubada endale tipptehnoloogiaid ning oma arendusmeeskondi, mille abil on neil võimalus implementeerida uusi liitreaalsuse tarkvaralahendusi. Samal ajal on aga tavalised firmad piiratud aja, meeskonna ja raha poolest, mis omakorda sunnib neid kasutama turul olemasolevaid lahendusi - tööriistakomplekte.Sellest lähtuvalt keskendub käesolev töö vajalikele teadmistele, mida läheb vaja erinevate liitreaalsuse tööriistakomplektide kasutamisel. Selleks, et luua edukalt valmis liitreaalsuse tarkvara, on välja valitud kindlad raamistikud, millest koostatakse ülevaade, mida testitakse ning võrreldakse. Lisaks sellele õpetatakse uurimise käigus selgeks ka mõned põhiteadmised liitreaalsuse arendamiseks Androidi platvormi näitel.Augmented Reality (AR) is an emerging technology. Besides entertainment, AR also is found to be used in medicine, military, engineering and other major fields of enterprise and government. Regardless of the application area, development teams usually target to achieve best performance and visual results in the AR software that they are providing. In addition, the core technology used behind a particular AR software depends a lot on resources available to the team. This means, that organizations with large resources can afford to implement AR software solutions using cutting-edge technologies build by their own engineering units, whereas ordinary companies are usually limited in time, staff and budget. Hence, forcing them to use existing market solutions - toolkits.From this perspective, this thesis work focuses on providing the basics of working with AR toolkits. In order to succeed in building an AR software, particular toolkits are selected to be reviewed, tested and compared. Moreover, during the investigation process some essentials of the AR development under Android platform are also studied