Spott : on-the-spot e-commerce for television using deep learning-based video analysis techniques

Spott is an innovative second screen mobile multimedia application which offers viewers relevant information on objects (e.g., clothing, furniture, food) they see and like on their television screens. The application enables interaction between TV audiences and brands, so producers and advertisers can offer potential consumers tailored promotions, e-shop items, and/or free samples. In line with the current views on innovation management, the technological excellence of the Spott application is coupled with iterative user involvement throughout the entire development process. This article discusses both of these aspects and how they impact each other. First, we focus on the technological building blocks that facilitate the (semi-) automatic interactive tagging process of objects in the video streams. The majority of these building blocks extensively make use of novel and state-of-the-art deep learning concepts and methodologies. We show how these deep learning based video analysis techniques facilitate video summarization, semantic keyframe clustering, and (similar) object retrieval. Secondly, we provide insights in user tests that have been performed to evaluate and optimize the application's user experience. The lessons learned from these open field tests have already been an essential input in the technology development and will further shape the future modifications to the Spott application

Analyzing the impact of mobile technology on mobile-centric youth in South Africa

Includes abstract.Includes bibliographical references.Our research documents the successful development of a system for stakeholders in the low-income urban environment, to facilitate the dissemination of information through enhanced mobile technology. The research project took place in Khayelitsha, Cape Town. Before beginning the formal study we entered the environment to better understand mobile technology practices amongst low-income urban teenagers and how we could supplement these practices by providing a means of media dissemination to the relevant stakeholders in the environment. We allied ourselves with two local stakeholders, the Ikamva Youth NGO and the Nazeema Isaacs local library staff. These organizations would provide us with access to low-income urban teenagers, and they were interested in adopting alternative technology to disseminate media to these youth

Active learning continuum techniques to develop vocabulary subskill on elementary school students at “República del Ecuador” school in Otavalo during the academic year 2021-2022

To integrate Active Learning Continuum Techniques in the development of vocabulary subskill on elementary school students at “República del Ecuador” School in Otavalo during the academic year 2021-2022.El presente trabajo de investigación sobre las técnicas continuas de aprendizaje activo para desarrollar la subdestreza de vocabulario, ha sido llevada a cabo debido a que en la enseñanza del idioma Inglés como Lengua Extranjera se siguen utilizando metodologías tradicionales en las que el docente es el actor principal del proceso de enseñanza-aprendizaje y el estudiante desempeña un papel secundario en la adquisición y construcción del conocimiento. Además, en la enseñanza del idioma Inglés como lengua Extranjera al desarrollo de la subdestreza de vocabulario se le ha dado poca importancia a pesar de ser ésta la base fundamental para el aprendizaje de cualquier idioma ya que el conocimiento de vocabulario es el prerrequisito para el desarrollo de macro habilidades del idioma como leer, escribir, hablar y escuchar. Esencialmente, el objetivo principal de esta investigación se concentra en lograr que los estudiantes de 5° Año de Educación General Básica de la Unidad Educativa “República del Ecuador” desarrollen la subdestreza de vocabulario en Inglés acorde a su edad, intereses y el currículo educativo vigente a través de técnicas continuas de aprendizaje activo. En esta investigación se optó por aplicar una metodología mixta de investigación para garantizar la viabilidad de este estudio. Uno de los hallazgos más significativos de esta investigación fue que los estudiantes están interesados en aprender vocabulario a través de actividades divertidas que impliquen el uso de tecnología, la interacción con sus compañeros y el desarrollo de la creatividad. En otras palabras, a los estudiantes les gustaría desarrollar esta habilidad de manera interactiva. Por lo tanto, el manual didáctico propuesto en este estudio fue diseñado siguiendo los principios del Aprendizaje Activo para transformar las clases tradicionales en verdaderos espacios de aprendizaje donde el estudiante desarrolle independencia, creatividad, autonomía, habilidades para trabajar en equipo y pensamiento crítico para resolver problemas.Maestrí

Towards solving computer vision problems: datasets, labels, algorithms, and applications

The solution to a supervised computer vision problem consists of an application, algorithm, input data, and a set of human generated labels. Solving these kinds of tasks involves collecting large quantities of data, collecting appropriate labels, and developing machine vision algorithms tailored to the application. Progress on these problems has often benefited from large scale datasets with high fidelity labels. Successful algorithms display a synergy between application goals and the size and quality of the dataset. This thesis presents work highlighting the importance of each component of a supervised vision task.First, the problem of automatically classifying groups of people into social categories is introduced. This problem is called Urban Tribe Classification. To tackle this problem, each individual and the entire group of individuals are modeled. Since this was a newly introduced computer vision problem, a dataset for this task was created. On this dataset, the combined representation of group and individuals outperforms using only the person representations. This model showed promising results for automatic subculture classification.Second, the problem of creating perceptual embeddings based on human similarity judgements is tackled. This work focuses on triplet similarity comparisons of the form ``Is object $i$ more similar to $j$ or $k$?'', which have been useful for computer vision and machine learning applications. Unfortunately, triplet similarity comparisons, like many human labeling efforts, can be prohibitively expensive. This work proposes two techniques for dealing with this obstacle. First, an alternative display for collecting triplets is designed. This display shows a probe image and a grid of query images, allowing the user to collect multiple triplets simultaneously. The display is shown to reduce the cost and time of triplet collection. In addition, higher quality embeddings are created with the improved triplet collection UI. A 10,000-food item dataset of human taste similarity was created using this UI. Second, ``SNaCK,'' a low-dimensional perceptual embedding algorithm that combines human expertise with automatic machine kernels, is introduced. Both parts are complementary: human insight can capture relationships that are not apparent from the object's visual similarity and the machine can help relieve the human from having to exhaustively specify many constraints. Finally, the precise localization of key frames of an action is explored. This work focuses on detecting the exact starting frame of a behavior, an important task for neuroscience research. To address this problem, a loss designed to penalize extra and missed action start detections over small misalignments. Recurrent neural networks (RNN) are trained to optimize this loss. The model is shown to reduce the number of false positives, an important criteria defined by the neuroscientist. The performance of the model is evaluated on a new dataset, the Mouse Reach Dataset, a large, annotated video dataset of mice performing a sequence of actions. The dataset was created for neuroscience research. On this dataset, the proposed model outperforms related approaches and baseline methods using an unstructured loss

A video-based automated recommender (VAR) system for garments

In this paper, we propose an automated and scalable garment recommender system using real-time in-store videos that can improve the experiences of garment shoppers and increase product sales. The video-based automated recommender (VAR) system is based on observations that garment shoppers tend to try on garments and evaluate themselves in front of store mirrors. Combining state-of-the-art computer vision techniques with marketing models of consumer preferences, the system automatically identifies shoppers’ preferences based on their reactions and uses that information to make meaningful personalized recommendations. First, the system uses a camera to capture a shopper’s behavior in front of the mirror to make inferences about her preferences based on her facial expressions and the part of the garment she is examining at each time point. Second, the system identifies shoppers with preferences similar to the focal customer from a database of shoppers whose preferences, purchasing, and/or consideration decisions are known. Finally, recommendations are made to the focal customer based on the preferences, purchasing, and/or consideration decisions of these like-minded shoppers. Each of the three steps can be implemented with several variations, and a retailing chain can choose the specific configuration that best serves its purpose. In this paper, we present an empirical test that compares one specific type of VAR system implementation against two alternative, nonautomated personal recommender systems: self-explicated conjoint (SEC) and self-evaluation after try-on (SET). The results show that VAR consistently outperforms SEC and SET. A second empirical study demonstrates the feasibility of VAR in real-time applications. Participants in the second study enjoyed the VAR experience, and almost all of them tried on the recommended garments. VAR should prove to be a valuable tool for both garment retailers and shoppers. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0984.The authors thank the participants in presentations given by the authors in College of Business at City Univeristy of HongKong and Cambridge Judge Business School for their feedback, as well as the Editor, the Area Editor, and two anonymous Marketing Science reviewers for their insightful comments. This research was supported by two National Natural Science Foundation of China Fund (Grants 71232008 & 71502039), and the Institute for Sustainable Innovation and Growth (iSIG) at School of Management, Fudan University

The influence of marketing media on tweens propensity to consume electronic goods.

Doctor of Philosophy in Management. University of KwaZulu-Natal, Pietermaritzburg, 2017.Tweens are a different regiment of children who are not reflected as children, but have not technologically advanced into fully autonomous teenagers (Hulan, 2007:31). This target market is viewed as the, “richest generation of children” (Lindstrom, 2004:175). This market has disposable income and the ability to influence purchases which are endorsed, which results in the tween market being acknowledged as a commercial niche market. For an effective marketing campaign, marketing managers need to be mindful of children’s advertising knowledge, as well as the influence and pressure from peer endorsement on purchases which are made. As a result, to define the influence of these variables, a research tool (questionnaire) was given out to respondents involving 390 participants in primary and secondary schools situated in the KwaZulu Natal region, who successfully completed this research instrument. Questionnaires were administered to the tween market during life-orientation class as an exercise in the selected schools. Parent/Guardians consent had to be received before their child was able to participate in the study. Children were also given consent forms to fill in; to make the teacher aware if they wanted to partake in this study. The main aim of this study was to understand the influence marketing media has on tween market consumption, which produces their consumption patterns. Also with the different media channels developing, the marketing teams have to understand how this target market understands and interprets their messages. The key research objective was to define tweens advertising literacy at different stages and their propensity to consume advertising literacy, as well as the degree of parents’ and peers’ influence on their purchasing decisions. Understanding what factors influence the tween market was also looked at to determine their role in purchasing decisions. These factors will assist marketers and professionals when margin structuring marketing and media campaigns aimed at the tween market. Generation Z is defined as the tween market and this was also looked at in detail; it consisted of character, personality and preferences. Their consumption of media and digital media was looked at in detail as it influences marketing campaigns. Data was analyzed using SPSS (Statistics Package for Social Sciences). Findings from the data collection were signified, and compared to the collected works gathered, and an emphasis was on Roedder’s information processing model and Piaget’s Hierarchy of Cognitive development, (Roedder, 1981:145; Piaget, 1960:135). The data gathered was displayed in various chart formats and tables to form an illustration of the results. The results showed there is a sturdy positive association amongst advertising literacy and the different age-groups. Peer influence also has an influence on the tween market purchase decision. It was also established the tween market is inclined to consume endorsed products, which have a high visibility during consumption, then goods with lower consumption conspicuousness. Parental influence also has a strong influence on the tween market and their purchase decisions. Other factors i.e. cultural & social, will also be looked at to gather insights on alternate factors which influence the target audience’s purchasing decisions. From the key gatherings, recommendations and insights were put together for South African managers and marketing professionals. This study report includes recommendations for research and considerations for marketing and media campaigns which will be aimed at tween’s and their purchase patterns. One of the recommendations marketing professional’s states campaigns should consider advertising to the tween market as well, instead of forgetting because they are a niche segment. Empirical findings show children are more naïve consumers and more enthusiastic to buying goods. This makes them an attractive division, as they are eager consumers

Methods, Apparatuses And Computer Program Products For Analyzing Public Social Content And Providing Social Commerce Content Items To Entities

An online system for providing social commerce data to one or more network entities is disclosed. The online system may analyze public social content generated by one or more users associated with the online system such as, for example a social-networking system. The online system may determine similarities among data items associated with the social content. The online system may determine whether the data items associated with the social content relates to one or more products. The online system may classify one or more determined similar products into corresponding product groups. The online system may determine trends and insight data associated with the similar products. The online system may enable provision of the trends and insight data associated with the similar products to the one or more network entities

Fast human behavior analysis for scene understanding

Human behavior analysis has become an active topic of great interest and relevance for a number of applications and areas of research. The research in recent years has been considerably driven by the growing level of criminal behavior in large urban areas and increase of terroristic actions. Also, accurate behavior studies have been applied to sports analysis systems and are emerging in healthcare. When compared to conventional action recognition used in security applications, human behavior analysis techniques designed for embedded applications should satisfy the following technical requirements: (1) Behavior analysis should provide scalable and robust results; (2) High-processing efficiency to achieve (near) real-time operation with low-cost hardware; (3) Extensibility for multiple-camera setup including 3-D modeling to facilitate human behavior understanding and description in various events. The key to our problem statement is that we intend to improve behavior analysis performance while preserving the efficiency of the designed techniques, to allow implementation in embedded environments. More specifically, we look into (1) fast multi-level algorithms incorporating specific domain knowledge, and (2) 3-D configuration techniques for overall enhanced performance. If possible, we explore the performance of the current behavior-analysis techniques for improving accuracy and scalability. To fulfill the above technical requirements and tackle the research problems, we propose a flexible behavior-analysis framework consisting of three processing-layers: (1) pixel-based processing (background modeling with pixel labeling), (2) object-based modeling (human detection, tracking and posture analysis), and (3) event-based analysis (semantic event understanding). In Chapter 3, we specifically contribute to the analysis of individual human behavior. A novel body representation is proposed for posture classification based on a silhouette feature. Only pure binary-shape information is used for posture classification without texture/color or any explicit body models. To this end, we have studied an efficient HV-PCA shape-based descriptor with temporal modeling, which achieves a posture-recognition accuracy rate of about 86% and outperforms other existing proposals. As our human motion scheme is efficient and achieves a fast performance (6-8 frames/second), it enables a fast surveillance system or further analysis of human behavior. In addition, a body-part detection approach is presented. The color and body ratio are combined to provide clues for human body detection and classification. The conventional assumption of up-right body posture is not required. Afterwards, we design and construct a specific framework for fast algorithms and apply them in two applications: tennis sports analysis and surveillance. Chapter 4 deals with tennis sports analysis and presents an automatic real-time system for multi-level analysis of tennis video sequences. First, we employ a 3-D camera model to bridge the pixel-level, object-level and scene-level of tennis sports analysis. Second, a weighted linear model combining the visual cues in the real-world domain is proposed to identify various events. The experimentally found event extraction rate of the system is about 90%. Also, audio signals are combined to enhance the scene analysis performance. The complete proposed application is efficient enough to obtain a real-time or near real-time performance (2-3 frames/second for 720×576 resolution, and 5-7 frames/second for 320×240 resolution, with a P-IV PC running at 3GHz). Chapter 5 addresses surveillance and presents a full real-time behavior-analysis framework, featuring layers at pixel, object, event and visualization level. More specifically, this framework captures the human motion, classifies its posture, infers the semantic event exploiting interaction modeling, and performs the 3-D scene reconstruction. We have introduced our system design based on a specific software architecture, by employing the well-known "4+1" view model. In addition, human behavior analysis algorithms are directly designed for real-time operation and embedded in an experimental runtime AV content-analysis architecture. This executable system is designed to be generic for multiple streaming applications with component-based architectures. To evaluate the performance, we have applied this networked system in a single-camera setup. The experimental platform operates with two Pentium Quadcore engines (2.33 GHz) and 4-GB memory. Performance evaluations have shown that this networked framework is efficient and achieves a fast performance (13-15 frames/second) for monocular video sequences. Moreover, a dual-camera setup is tested within the behavior-analysis framework. After automatic camera calibration is conducted, the 3-D reconstruction and communication among different cameras are achieved. The extra view in the multi-camera setup improves the human tracking and event detection in case of occlusion. This extension of multiple-view fusion improves the event-based semantic analysis by 8.3-16.7% in accuracy rate. The detailed studies of two experimental intelligent applications, i.e., tennis sports analysis and surveillance, have proven their value in several extensive tests in the framework of the European Candela and Cantata ITEA research programs, where our proposed system has demonstrated competitive performance with respect to accuracy and efficiency