Argonauta Video Database: Real operation. Scene 1 (Right camera)

Real operation scene This scene was recorded during a real Irradiation operation, more specifically during its final tasks (removing the irradiated sample). This scene was an extra recording to the script and planned ones. - Scene: Involved a number of persons, as: two operators, two personnel belonging to the radiological protection service, and the "client" who asked for the irradiation. Video file labels: "20140402150657_IPCAM": recorded by the right camera.

Argonauta Video Database: Real operation. Scene 1 (Left camera)

Real operation scene This scene was recorded during a real Irradiation operation, more specifically during its final tasks (removing the irradiated sample). This scene was an extra recording to the script and planned ones. - Scene: Involved a number of persons, as: two operators, two personnel belonging to the radiological protection service, and the "client" who asked for the irradiation. Video file labels: "20140402150658_IPCAM": recorded by the left camera.

Argonauta Video Database: Projection and Homography matrices. Projection Left Camera

Description of the Annotation files: Annotation files are supplied for each video, for benchmarking. Annotations correspond to ground truths of peoples' positions in the image plane, and also for their feet positions, when they were visible. Annotations were performed manually, with the aid of a code developed by (Silva et al., 2014; see the Thesis for details). Targets (people or feet) are marked at variable frame intervals and then linearly interpolated.

Argonauta Video Database: Projection and Homography matrices. Projection Right Camera

Description of the Annotation files: Annotation files are supplied for each video, for benchmarking. Annotations correspond to ground truths of peoples' positions in the image plane, and also for their feet positions, when they were visible. Annotations were performed manually, with the aid of a code developed by (Silva et al., 2014; see the Thesis for details). Targets (people or feet) are marked at variable frame intervals and then linearly interpolated.

Argonauta Video Database: Projection and Homography matrices. Homography Right Camera

Description of the Annotation files: Annotation files are supplied for each video, for benchmarking. Annotations correspond to ground truths of peoples' positions in the image plane, and also for their feet positions, when they were visible. Annotations were performed manually, with the aid of a code developed by (Silva et al., 2014; see the Thesis for details). Targets (people or feet) are marked at variable frame intervals and then linearly interpolated.

Argonauta Video Database: Projection and Homography matrices. Homography Left Camera

Description of the Annotation files: Annotation files are supplied for each video, for benchmarking. Annotations correspond to ground truths of peoples' positions in the image plane, and also for their feet positions, when they were visible. Annotations were performed manually, with the aid of a code developed by (Silva et al., 2014; see the Thesis for details). Targets (people or feet) are marked at variable frame intervals and then linearly interpolated.

Argonauta Video Database. Data Agregation

This Database was generated during the development of a computer vision-based system for safety purposes in nuclear plants. The system aims at detecting and tracking people within a nuclear plant. Further details may be found in the related thesis. The research was developed through a cooperation between the Graduate Electrical Engineering Program of Federal University of Rio de Janeiro (PEE/COPPE, UFRJ) and the Nuclear Engineering Institute of National Commission of Nuclear Energy (IEN, CNEN). The experimental part of this research was carried out in Argonauta, a nuclear research reactor belonging to IEN. The Database is made available in the sequel. All the videos are already rectified. The Projection and Homography matrices are given in the end, for both cameras. Please, acknowledge the use of this Database in any publication.

Compression Efficiency Analysis of Wyner-Ziv Video Coding with Motion Compensated Side Information Interpolation

The Wyner-Ziv video coding (WZVC) rate distortion performance is highly dependent on the quality of the side information, an estimation of the original frame, created at the decoder. This paper, characterizes the WZVC efficiency when motion compensated frame interpolation (MCFI) techniques are used to generate the side information, a difficult problem in WZVC especially because the decoder only has available some reference decoded frames. The proposed WZVC compression efficiency rate model relates the power spectral of the estimation error to the accuracy of the MCFI motion field. Then, some interesting conclusions may be derived related to the impact of the motion field smoothness and the correlation to the true motion trajectories on the compression performance.

Low Complexity Intra Mode Selection for Efficient Distributed Video Coding

Motion compensated frame interpolation (MCFI) is one of the most efficient solutions to generate side information (SI) in the context of distributed video coding. However, it creates SI with rather significant motion compensated errors for some frame regions while rather small for some other regions depending on the video content. In this paper, a low complexity Infra mode selection algorithm is proposed to select the most 'critical' blocks in the WZ frame and help the decoder with some reliable data for those blocks. For each block, the novel coding mode selection algorithm estimates the encoding rate for the Intra based and WZ coding modes and determines the best coding mode while maintaining a low encoder complexity. The proposed solution is evaluated in terms of rate-distortion performance with improvements up to 1.2 dB regarding a WZ coding mode only solution.

Advanced side information creation techniques and framework for Wyner-Ziv video coding

Recently, several distributed video coding (DVC) solutions based on the distributed source coding (DSC) paradigm have appeared in the literature. Wyner-Ziv (WZ) video coding, a particular case of DVC where side information is made available at the decoder, enable to achieve a flexible distribution of the computational complexity between the encoder and decoder, promising to fulfill novel requirements from applications such as video surveillance, sensor networks and mobile camera phones. The quality of the side information at the decoder has a critical role in determining the WZ video coding rate-distortion (RD) performance, notably to raise it to a level as close as possible to the RD performance of standard predictive video coding schemes. Towards this target, efficient motion search algorithms for powerful frame interpolation are much needed at the decoder. In this paper, the RD performance of a Wyner-Ziv video codec is improved by using novel, advanced motion compensated frame interpolation techniques to generate the side information. The development of these type of side information estimators is a difficult problem in WZ video coding, especially because the decoder only has available some reference, decoded frames. Based on the regularization of the motion field, novel side information creation techniques are proposed in this paper along with a new frame interpolation framework able to generate higher quality side information at the decoder. To illustrate the RD performance improvements, this novel side information creation framework has been integrated in a transform domain turbo coding based Wyner-Ziv video codec. Experimental results show that the novel side information creation solution leads to better RD performance than available state-of-the-art side information estimators, with improvements up to 2 dB: moreover, it allows outperforming H.264/AVC Intra by up to 3 dB with a lower encoding complexity.

Personalised placement in networked video

Personalised video can be achieved by inserting objects into a video play-out according to the viewer's profile. Content which has been authored and produced for general broadcast can take on additional commercial service features when personalised either for individual viewers or for groups of viewers participating in entertainment, training, gaming or informational activities. Although several scenarios and use-cases can be envisaged, we are focussed on the application of personalised product placement. Targeted advertising and product placement are currently garnering intense interest in the commercial networked media industries. Personalisation of product placement is a relevant and timely service for next generation online marketing and advertising and for many other revenue generating interactive services. This paper discusses the acquisition and insertion of media objects into a TV video play-out stream where the objects are determined by the profile of the viewer. The technology is based on MPEG-4 standards using object based video and MPEG-7 for metadata. No proprietary technology or protocol is proposed. To trade the objects into the video play-out, a Software-as-a-Service brokerage platform based on intelligent agent technology is adopted. Agencies, libraries and service providers are represented in a commercial negotiation to facilitate the contractual selection and usage of objects to be inserted into the video play-out.

Análise de Desempenho e Recomendação para Soluções IVR

Mestrado em Engenharia Informática, Área de Especialização em Tecnologias do Conhecimento e da Decisão

Behind video lectures in a MOOC

The year 2012 was the “boom year” in MOOC and all its outstanding growth until now, made us move forward in designing the first MOOC in our Institution (and the third in our country, Portugal). Most MOOC are video lectured based and the learning analytic process to these ones is just taking its first steps. Designing a video-lecture seems, at a first glance, very easy: one can just record a live lesson or lecture and turn it, directly, into a video-lecture (even here one may experience some “sound” and “camera” problems); but developing some engaging, appealing video-lecture, that motivates students to embrace knowledge and that really contributes to the teaching/learning process, it is not an easy task. Therefore questions like: “What kind of information can induce knowledge construction, in a video-lecture?”, “How can a professor interact in a video-lecture when he is not really there?”, “What are the video-lectures attributes that contribute the most to viewer’s engagement?”, “What seems to be the maximum “time-resistance” of a viewer?”, and many others, raised in our minds when designing video-lectures to a Mathematics MOOC from the scratch. We believe this technological resource can be a powerful tool to enhance students' learning process. Students that were born in digital/image era, respond and react slightly different to outside stimulus, than their teachers/professors ever did or do. In this article we will describe just how we have tried to overcome some of the difficulties and challenges we tackled when producing our own video-math-lectures and in what way, we feel, videos can contribute to the teaching and learning process at higher education level.

Profissões : sinergias entre o video interactivo e o 3D estereoscópico

Trabalho de Projecto apresentado para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Novos Media e Práticas Web

Designing video lectures for MOOC

Educational videos differ from other teaching and learning technologies as they allow the benefit of using visual perception. Video lectures are not new to education, however with the use of innovative video technologies they can improve academic outcomes and extend the reach of education. They may offer extraordinary new experiences for higher education institutions (HEI). Through them lecturers can provide information and contents to students, and if used creatively, video lectures can become a powerful technological tool in education, inside and outside classrooms. Inside a classroom it can motivate students and improve topics’ debate and outside it is a good support for students’ self- learning. In some cases they can be used to work some subjects standing behind, but needed to support actual courses contents, that students do not remember (or were not even taught), opening an “in front to the past door” that backs students self-study. The student-educator dynamic is changing. Students are expecting exceptional instruction and educators are expecting students to be more and more well informed about subjects from online viewing.This article explores some of the potential benefits and challenges associated with the use of video lectures in the teaching and learning process at higher education. We will also discuss some thoughts and examples for the use of teaching materials to enhance student’s learning and try to understand how video can act as powerful and innovative to enlighten teaching and learning (note that unfortunately, sometimes, the opposite is happening).