Automated camera ranking and selection using video content and scene context

PhDWhen observing a scene with multiple cameras, an important problem to solve is to automatically identify “what camera feed should be shown and when?” The answer to this question is of interest for a number of applications and scenarios ranging from sports to surveillance. In this thesis we present a framework for the ranking of each video frame and camera across time and the camera network, respectively. This ranking is then used for automated video production. In the first stage information from each camera view and from the objects in it is extracted and represented in a way that allows for object- and frame-ranking. First objects are detected and ranked within and across camera views. This ranking takes into account both visible and contextual information related to the object. Then content ranking is performed based on the objects in the view and camera-network level information. We propose two novel techniques for content ranking namely: Routing Based Ranking (RBR) and Multivariate Gaussian based Ranking (MVG). In RBR we use a rule based framework where weighted fusion of object and frame level information takes place while in MVG the rank is estimated as a multivariate Gaussian distribution. Through experimental and subjective validation we demonstrate that the proposed content ranking strategies allows the identification of the best-camera at each time. The second part of the thesis focuses on the automatic generation of N-to-1 videos based on the ranked content. We demonstrate that in such production settings it is undesirable to have frequent inter-camera switching. Thus motivating the need for a compromise, between selecting the best camera most of the time and minimising the frequent inter-camera switching, we demonstrate that state-of-the-art techniques for this task are inadequate and fail in dynamic scenes. We propose three novel methods for automated camera selection. The first method (¡go f ) performs a joint optimization of a cost function that depends on both the view quality and inter-camera switching so that a i Abstract ii pleasing best-view video sequence can be composed. The other two methods (¡dbn and ¡util) include the selection decision into the ranking-strategy. In ¡dbn we model the best-camera selection as a state sequence via Directed Acyclic Graphs (DAG) designed as a Dynamic Bayesian Network (DBN), which encodes the contextual knowledge about the camera network and employs the past information to minimize the inter camera switches. In comparison ¡util utilizes the past as well as the future information in a Partially Observable Markov Decision Process (POMDP) where the camera-selection at a certain time is influenced by the past information and its repercussions in the future. The performance of the proposed approach is demonstrated on multiple real and synthetic multi-camera setups. We compare the proposed architectures with various baseline methods with encouraging results. The performance of the proposed approaches is also validated through extensive subjective testing

An intelligent content discovery technique for health portal content management

Background: Continuous content management of health information portals is a feature vital for its sustainability and widespread acceptance. Knowledge and experience of a domain expert is essential for content management in the health domain. The rate of generation of online health resources is exponential and thereby manual examination for relevance to a specific topic and audience is a formidable challenge for domain experts. Intelligent content discovery for effective content management is a less researched topic. An existing expert-endorsed content repository can provide the necessary leverage to automatically identify relevant resources and evaluate qualitative metrics.Objective: This paper reports on the design research towards an intelligent technique for automated content discovery and ranking for health information portals. The proposed technique aims to improve efficiency of the current mostly manual process of portal content management by utilising an existing expert-endorsed content repository as a supporting base and a benchmark to evaluate the suitability of new content.Methods: A model for content management was established based on a field study of potential users. The proposed technique is integral to this content management model and executes in several phases (ie, query construction, content search, text analytics and fuzzy multi-criteria ranking). The construction of multi-dimensional search queries with input from Wordnet, the use of multi-word and single-word terms as representative semantics for text analytics and the use of fuzzy multi-criteria ranking for subjective evaluation of quality metrics are original contributions reported in this paper.Results: The feasibility of the proposed technique was examined with experiments conducted on an actual health information portal, the BCKOnline portal. Both intermediary and final results generated by the technique are presented in the paper and these help to establish benefits of the technique and its contribution towards effective content management.Conclusions: The prevalence of large numbers of online health resources is a key obstacle for domain experts involved in content management of health information portals and websites. The proposed technique has proven successful at search and identification of resources and the measurement of their relevance. It can be used to support the domain expert in content management and thereby ensure the health portal is up-to-date and current

Network-based ranking in social systems: three challenges

Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure

Use of the TOPSIS technique to choose the best supplier of quarry natural aggregate

Purpose. All over the world, natural substance – the most consumed after water – is the aggregate. The aim of this paper is to select the best supplier of Quarry Natural Aggregate (QNA). Methods. Selection of the best supplier of QNA is performed using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach, and the method of weights based on ordinal ranking of criteria, and Lagrange multiplier. Findings. In this article, the proposed Multi-Criteria Decision Making (MCDM) approach helps the decision maker(s) to choose the best supplier of QNA amongst the considered and evaluated suppliers. Originality. During negotiation with suppliers, many are the decision makers which only attach an importance at two criteria (unit price and quality, or unit price and delivery time). Thereby, other criteria are not taken into account. Consequently, supplier selection would become not-efficient. The originality of this work is based on the multi-criteria approach to choose the best supplier of QNA. Practical implications. The efficient choice of the best supplier of QNA represents a practical and economical value for the enterprises of the civil engineering, public works, railway and hydraulic works.Мета. Обґрунтування та вибір оптимального постачальника кар’єрного щебню як природного заповнювача на основі використання багатокритеріального методу. Методика. Вибір найкращого постачальника кар’єрного природного заповнювача здійснювався за допомогою багатокритеріального методу аналізу варіантів за ступенем близькості до оптимального (TOPSIS) і методу вагових коефіцієнтів на основі порядкового ранжирування критеріїв та множника Лагранжа. Результати. Підхід, що описується в статті, заснований на багатокритеріальному прийнятті рішень і дозволяє обрати кращого постачальника природного заповнювача серед наявних та розглянутих на ринку компаній. В якості ілюстрації запропонована методологія застосована до чисельного прикладу. Це дозволило визначити вагу впливових на оцінку критеріїв, оцінку значень характеристик кожного розглянутого постачальника QNA, встановлення рейтингу розглянутих постачальників QNA і вибір альтернативи {a4} в якості кращого постачальника QNA. Наукова новизна. Вперше для вибору оптимального постачальника природного заповнювача крім факторів ціни і якості встановлено характер впливу на загальну оцінку також ряду інших факторів: вартість транспортування, транспортна відстань, час доставки, гарантійна політика й рівень відхилення. У даній роботі вперше пропонується багатокритеріальний підхід до вибору оптимального постачальника природного заповнювача кар’єра. Практична значимість. Ефективний вибір постачальника природного заповнювача кар’єра важливий з практичної та економічної точок зору для підприємств у галузі цивільного будівництва, громадських робіт, залізниці та гідротехнічних споруд.Цель. Обоснование и выбор оптимального поставщика карьерного щебня как природного заполнителя на основе использования многокритериального метода. Методика. Выбор лучшего поставщика карьерного природного заполнителя осуществлялся с помощью многокритериального метода анализа вариантов по степени близости к оптимальному (TOPSIS) и метода весовых коэффициентов на основе порядкового ранжирования критериев и множителя Лагранжа. Результаты. Подход, описываемый в статье, основан на многокритериальном принятии решений и позволяет выбрать лучшего поставщика природного заполнителя среди имеющихся и рассматриваемых на рынке компаний. В качестве иллюстрации предложенная методология применена к числовому примеру. Это позволило определить вес влияющих на оценку критериев, оценку значений характеристик каждого рассматриваемого поставщика QNA, установление рейтинга рассматриваемых поставщиков QNA и выбор альтернативы {a4} в качестве лучшего поставщика QNA. Научная новизна. Впервые для выбора оптимального поставщика природного заполнителя кроме факторов цены и качества установлен характер влияния на общую оценку также ряда других факторов: стоимость транспортирования, транспортное расстояние, время доставки, гарантийная политика и уровень отклонения. В данной работе впервые предлагается многокритериальный подход к выбору оптимального поставщика природного заполнителя карьера. Практическая значимость. Эффективный выбор поставщика природного заполнителя карьера важен с практической и экономической точек зрения для предприятий в области гражданского строительства, общественных работ, железной дороги и гидротехнических сооружений.The authors thank all the colleagues which have contributed to the realization of this research work