A Fuzzy Logic-Based System for Soccer Video Scenes Classification

Massive global video surveillance worldwide captures data but lacks detailed activity information to flag events of interest, while the human burden of monitoring video footage is untenable. Artificial intelligence (AI) can be applied to raw video footage to identify and extract required information and summarize it in linguistic formats. Video summarization automation usually involves text-based data such as subtitles, segmenting text and semantics, with little attention to video summarization in the processing of video footage only. Classification problems in recorded videos are often very complex and uncertain due to the dynamic nature of the video sequence and light conditions, background, camera angle, occlusions, indistinguishable scene features, etc. Video scene classification forms the basis of linguistic video summarization, an open research problem with major commercial importance. Soccer video scenes present added challenges due to specific objects and events with similar features (e.g. “people” include audiences, coaches, and players), as well as being constituted from a series of quickly changing and dynamic frames with small inter-frame variations. There is an added difficulty associated with the need to have light weight video classification systems working in real time with massive data sizes. In this thesis, we introduce a novel system based on Interval Type-2 Fuzzy Logic Classification Systems (IT2FLCS) whose parameters are optimized by the Big Bang–Big Crunch (BB-BC) algorithm, which allows for the automatic scenes classification using optimized rules in broadcasted soccer matches video. The type-2 fuzzy logic systems would be unequivocal to present a highly interpretable and transparent model which is very suitable for the handling the encountered uncertainties in video footages and converting the accumulated data to linguistic formats which can be easily stored and analysed. Meanwhile the traditional black box techniques, such as support vector machines (SVMs) and neural networks, do not provide models which could be easily analysed and understood by human users. The BB-BC optimization is a heuristic, population-based evolutionary approach which is characterized by the ease of implementation, fast convergence and low computational cost. We employed the BB-BC to optimize our system parameters of fuzzy logic membership functions and fuzzy rules. Using the BB-BC we are able to balance the system transparency (through generating a small rule set) together with increasing the accuracy of scene classification. Thus, the proposed fuzzy-based system allows achieving relatively high classification accuracy with a small number of rules thus increasing the system interpretability and allowing its real-time processing. The type-2 Fuzzy Logic Classification System (T2FLCS) obtained 87.57% prediction accuracy in the scene classification of our testing group data which is better than the type-1 fuzzy classification system and neural networks counterparts. The BB-BC optimization algorithms decrease the size of rule bases both in T1FLCS and T2FLCS; the T2FLCS finally got 85.716% with reduce rules, outperforming the T1FLCS and neural network counterparts, especially in the “out-of-range data” which validates the T2FLCSs capability to handle the high level of faced uncertainties. We also presented a novel approach based on the scenes classification system combined with the dynamic time warping algorithm to implement the video events detection for real world processing. The proposed system could run on recorded or live video clips and output a label to describe the event in order to provide the high level summarization of the videos to the user

Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term “Big Video Data” (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this paper, we draw researchers’ attention towards the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook towards future research directions derived from our critical assessment of the efforts invested so far in this exciting field

Video game subcultures: playing at the periphery of mainstream culture [special issue of GAME Journal]

This issue of GAME Journal offers an overview and a series of case studies on video games from the point of view of subcultural theory. There has been little work in game studies from this perspective, which offers a theoretical frame for the ever growing complexity of the audiences involved with the medium of the video game. The study of subcultures on the other hand has a long standing and complex tradition which culminates in what has been recently defined as the “post-subcultural” theoretical scenario

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Bloody Disgusting! Abjection, Excess & Absurdity: The Carnivalesque Cohesion Between Horror & Comedy in Film & Television

Horror and comedy. Screaming and laughing. Two genres and the visceral responses which they provoke, broadly considered to be polarised, apparently juxtaposed. This thesis argues that horror and comedy can be significantly more cohesive in their thematic traits, visual presentation and narrative events, than might initially be considered. Expanding a relatively underexplored academic field and building on the work of Paul (1994), the doctorate explores gross-out cinema and television in both theory and praxis. Part One opens with scholarly exploration of core theories of genre, horror and comedy. Semiotic and historical analysis and close reading of key texts in the horror, comedy, and hybrid horror comedy genre identifies and considers shared representation across the genres. Analysed texts include The Evil Dead series (1981-1992), Grimsby (2016), Nighty Night (2004-2005) and Braindead (1992). The core shared themes and representations across the genres are posited as abjection, excess and absurdity. Each of these elements is then explored in context of the tension of horror and humour co-present in the grotesque (Thomson, 1972). The paradoxical pleasure in reception (often in the disgust response) is found to align to the transgressions of the carnivalesque, and moreover, the carnivalesque grotesque (Danow, 1995, Bakhtin, 1974 et al.). These findings are then uniquely applied in praxis in Part Two in the original feature lengthfilm script Knitters! in which the women of the Potter’s Bluff Townswomen’s Guild must face an indestructible supernatural foe in an isolated Lake District resort. In the Lake District no-one can hear you scream! The Part Three exegesis reflects rigorously on the application of thesis findings in praxis, alongside detailed gnosis of the practical construction of a feature length script including close consideration of plotting, narrative pacing and characterisation

How Do You Squash a Cricket? A Collection of Essays

Essays include: How Do You Squash a Cricket? Imagine That The Night of Their Lives Not A Member Coming of Age in a Catholic School Bathroom The Perils of Being Pleasant Single White Female Seeks Fairy Godmother Parents These Days Losing My Religion A Good Enough Samarita

The Whitworthian 2007-2008

The Whitworthian student newspaper, September 2007-April 2008.https://digitalcommons.whitworth.edu/whitworthian/1092/thumbnail.jp