Machine learning-based human observer analysis of video sequences

The research contributes to the field of video analysis by proposing novel approaches to automatically generating human observer performance patterns that can be effectively used in advancing the modern video analytic and forensic algorithms. Eye tracker and eye movement analysis technology are employed in medical research, psychology, cognitive science and advertising. The data collected on human eye movement from the eye tracker can be analyzed using the machine and statistical learning approaches. Therefore, the study attempts to understand the visual attention pattern of people when observing a captured CCTV footage. It intends to prove whether the eye gaze of the observer which determines their behaviour is dependent on the given instructions or the knowledge they learn from the surveillance task. The research attempts to understand whether the attention of the observer on human objects is differently identified and tracked considering the different areas of the body of the tracked object. It attempts to know whether pattern analysis and machine learning can effectively replace the current conceptual and statistical approaches to the analysis of eye-tracking data captured within a CCTV surveillance task. A pilot study was employed that took around 30 minutes for each participant. It involved observing 13 different pre-recorded CCTV clips of public space. The participants are provided with a clear written description of the targets they should find in each video. The study included a total of 24 participants with varying levels of experience in analyzing CCTV video. A Tobii eye tracking system was employed to record the eye movements of the participants. The data captured by the eye tracking sensor is analyzed using statistical data analysis approaches like SPSS and machine learning algorithms using WEKA. The research concluded the existence of differences in behavioural patterns which could be used to classify participants of study is appropriate machine learning algorithms are employed. The research conducted on video analytics was perceived to be limited to few iii projects where the human object being observed was viewed as one object, and hence the detailed analysis of human observer attention pattern based on human body part articulation has not been investigated. All previous attempts in human observer visual attention pattern analysis on CCTV video analytics and forensics either used conceptual or statistical approaches. These methods were limited with regards to making predictions and the detection of hidden patterns. A novel approach to articulating human objects to be identified and tracked in a visual surveillance task led to constrained results, which demanded the use of advanced machine learning algorithms for classification of participants The research conducted within the context of this thesis resulted in several practical data collection and analysis challenges during formal CCTV operator based surveillance tasks. These made it difficult to obtain the appropriate cooperation from the expert operators of CCTV for data collection. Therefore, if expert operators were employed in the study rather than novice operator, a more discriminative and accurate classification would have been achieved. Machine learning approaches like ensemble learning and tree based algorithms can be applied in cases where a more detailed analysis of the human behaviour is needed. Traditional machine learning approaches are challenged by recent advances in the field of convolutional neural networks and deep learning. Therefore, future research can replace the traditional machine learning approaches employed in this study, with convolutional neural networks. The current research was limited to 13 different videos with different descriptions given to the participants for identifying and tracking different individuals. The research can be expanded to include any complicated demands with regards to changes in the analysis process

Project sanitarium:playing tuberculosis to its end game

Interdisciplinary and collaborative projects between industry and academia provide exceptional opportunities for learning. Project Sanitarium is a serious game for Windows PC and Tablet which aims to embed learning about tuberculosis (TB) through the player taking on the role of a doctor and solving cases across the globe. The project developed as a collaboration between staff and undergraduate students at the School of Arts, Media and Computer Games at Abertay University working with academics and researchers from the Infection Group at the University of St Andrews. The project also engaged industry partners Microsoft and DeltaDNA. The project aimed to educate students through a workplace simulation pedagogical model, encourage public engagement at events and through news coverage and lastly to prototype whether games could be used to simulate a virtual clinical trial. The project was embedded in the Abertay undergraduate programme where students are presented with real world problems to solve through design and technology. The result was a serious game prototype that utilized game design techniques and technology to demystify and educate players about the diagnosis and treatment of one of the world’s oldest and deadliest diseases, TB. Project Sanitarium aims to not only educate the player, but allows the player to become a part of a simulated drug trial that could potentially help create new treatments in the fight against TB. The game incorporates a mathematical model that is based on data from real-world drug trials. The interdisciplinary pedagogical model provides undergraduates with workplace simulation, wider industry collaboration and access to academic expertise to solve challenging and complex problems

Leveraging analytics to produce compelling and profitable film content

Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user generated content and original content produced by Subscription Video on Demand (SVOD) platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications

Deep Learning of Scene-Specific Classifier for Pedestrian Detection in Dubai

The performance of a generic pedestrian detector varies based on the data fed to it; when applied to a specific scene, its performance degrades dramatically, which require the detector to be fed with the specific target in mind so that it can produce the desired predictions and detect for the user the specified target. In this paper, I propose to feed the automated specialization of a scene-specific pedestrian detector, with multiple sources from pictures to even videos beginning with a generic video surveillance detector, however manually marking samples to ease the process, as the knowledge accumulated from the master program is still insufficient to produce high-end automated sample marking for the detector. The key idea is to consider a deep detector as a feature that produces a perception of the likelihood of a pedestrian being detected in the target. The system then will be fed with the manually marked samples to enhance its performance and the usage of an already existing system using the Monte Carlo sequential filter system. There has been the implementation of the pedestrian detectors in China, where it showcased the different patterns, the detector can classify and assess whether a pedestrian is present within the testing data or not. The project is truly fascinating as it shows how a machine can learn when fed with the right data and produce sensible results that lead to human renovation and up their living standards by decreasing the number of accidents related to pedestrians affecting the overall rate of accidents. “Many real-world data analysis tasks involve estimating unknown quantities from some given observations” as addressed by the authors within their report on Monte Carlo methods (Doucet A., de Freitas N., Gordon N.). In order to compute rational approximations, it is also important to follow numerical techniques. The techniques of Monte Carlo method (MCM) are powerful tools that allow us to achieve this objective (Andrieu C., Doucet A., Punskaya E.)

Getting Past It's Not For People Like Us: Pacific Northwest Ballet Builds a Following with Teens and Young Adults

This case study examines how the Pacific Northwest Ballet set about trying to cultivate the next generation of ballet-goers. Focusing on teens and adults under the age of 25, the Seattle-based ballet company sought in part to knock down the view of many young people that ballet is stuffy or boring and replace it with the view that ballet could be exciting and meaningful to them. The ballet company attacked the problem on a number of fronts, including revising promotional materials to appeal to younger audiences, posting online videos to familiarize viewers with the ballet, holding teen-only previews, and offering heavily discounted tickets. One result was a doubling over four years of ticket sales to teens

Oblique strategies for ambient journalism

Alfred Hermida recently posited ‘ambient journalism’ as a new framework for para- and professional journalists, who use social networks like Twitter for story sources, and as a news delivery platform. Beginning with this framework, this article explores the following questions: How does Hermida define ‘ambient journalism’ and what is its significance? Are there alternative definitions? What lessons do current platforms provide for the design of future, real-time platforms that ‘ambient journalists’ might use? What lessons does the work of Brian Eno provide–the musician and producer who coined the term ‘ambient music’ over three decades ago? My aim here is to formulate an alternative definition of ambient journalism that emphasises craft, skills acquisition, and the mental models of professional journalists, which are the foundations more generally for journalism practices. Rather than Hermida’s participatory media context I emphasise ‘institutional adaptiveness’: how journalists and newsrooms in media institutions rely on craft and skills, and how emerging platforms can augment these foundations, rather than replace them

Moving the Newsroom: Post-Industrial News Spaces and Places

Across the industry, we are seeing a wide-sweeping trend of newsrooms uprooting themselves: A.H. Belo, Gannett, MediaNews, Advance, Gate-House, Cox--small newspapers and large ones alike are listing their newsrooms for sale. Everyone from the private owners of the Syracuse Media Group, to the owners behind the Boston Herald, has moved buildings. This is a countrywide, size-wise trend1 that begs the questions: Is this just one more slap in the face for newsrooms now at their lowest staffing levels since the 1970s? Is it one more sign in line with the circulation declines and revenue slides that signals the inability of newsrooms to get their financial models right? Or perhaps, instead, there is another story to tell here, one embedded with the opportunity to think about a physical move as a way to shed the past and look forward to the future of news through tactile readjustment