A Compilation of Methods and Datasets for Group and Crowd Action Recognition

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group

Human Behaviour Recognition using Fuzzy System in Videos

Human behavior can be detected and analyzed using video sequence is a latest research topic in computer vision & machine learning. Human behavior is used as a basis for many modern applications, such as video surveillance, content-based information retrieval from videos etc. HBA (Human behaviour analysis) is tricky to design and develop due to uncertainty and ambiguity involved in people’s daily activities. To address this gap, we propose hierarchical structure combining TDNN, tracking algorithms, and fuzzy systems. As a result, HBA system performance will be improved in terms of robustness, effectiveness and scalability

Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

Final FLaReNet deliverable: Language Resources for the Future - The Future of Language Resources

Language Technologies (LT), together with their backbone, Language Resources (LR), provide an essential support to the challenge of Multilingualism and ICT of the future. The main task of language technologies is to bridge language barriers and to help creating a new environment where information flows smoothly across frontiers and languages, no matter the country, and the language, of origin. To achieve this goal, all players involved need to act as a community able to join forces on a set of shared priorities. However, until now the field of Language Resources and Technology has long suffered from an excess of individuality and fragmentation, with a lack of coherence concerning the priorities for the field, the direction to move, not to mention a common timeframe. The context encountered by the FLaReNet project was thus represented by an active field needing a coherence that can only be given by sharing common priorities and endeavours. FLaReNet has contributed to the creation of this coherence by gathering a wide community of experts and making them participate in the definition of an exhaustive set of recommendations

Modeling, enacting, and integrating custom crowdsourcing processes

Crowdsourcing (CS) is the outsourcing of a unit of work to a crowd of people via an open call for contributions. Thanks to the availability of online CS platforms, such as Amazon Mechanical Turk or CrowdFlower, the practice has experienced a tremendous growth over the past few years and demonstrated its viability in a variety of fields, such as data collection and analysis or human computation. Yet it is also increasingly struggling with the inherent limitations of these platforms: each platform has its own logic of how to crowdsource work (e.g., marketplace or contest), there is only very little support for structured work (work that requires the coordination of multiple tasks), and it is hard to integrate crowdsourced tasks into stateof-the-art business process management (BPM) or information systems. We attack these three shortcomings by (1) developing a flexible CS platform (we call it Crowd Computer, or CC) that allows one to program custom CS logics for individual and structured tasks, (2) devising a BPMN-based modeling language that allows one to program CC intuitively, (3) equipping the language with a dedicated visual editor, and (4) implementing CC on top of standard BPM technology that can easily be integrated into existing software and processes. We demonstrate the effectiveness of the approach with a case study on the crowd-based mining of mashup model patterns

Crowdsourcing: A Geographic Approach to Public Engagement, The Programmable City Working Paper 6

In this paper we examine three geographic crowdsourcing models, namely: volunteered geographic information (VGI), citizen science (CS) and participatory mapping (PM) (Goodchild, 2007; Audubon Society, 1900; and Peluso, 1995). We argue that these geographic knowledge producing practices can be adopted by governments to keep databases up to date (Budhathoki et al., 2008), to gain insight about natural resources (Conrad and Hilchey, 2011), to better understand the socio-economy of the people it governs (Johnston and Sieber, 2013) and as a form of data-based public engagement. The paper will be useful to governments and public agencies considering using geographic crowdsourcing in the future. We begin by defining VGI, CS, PM and crowdsourcing. Two typologies are then offered as methods to conceptualize these practices and the Kitchin (2014) data assemblage framework is proposed as a method by which state actors can critically examine their data infrastructures. A selection of exemplary VGI, CS and PM from Canada and the Republic of Ireland are discussed and the paper concludes with some high level recommendations for administrations considering a geographic approach to crowdsourcing

Crowdsourcing: A Geographic Approach to Public Engagement, The Programmable City Working Paper 6

In this paper we examine three geographic crowdsourcing models, namely: volunteered geographic information (VGI), citizen science (CS) and participatory mapping (PM) (Goodchild, 2007; Audubon Society, 1900; and Peluso, 1995). We argue that these geographic knowledge producing practices can be adopted by governments to keep databases up to date (Budhathoki et al., 2008), to gain insight about natural resources (Conrad and Hilchey, 2011), to better understand the socio-economy of the people it governs (Johnston and Sieber, 2013) and as a form of data-based public engagement. The paper will be useful to governments and public agencies considering using geographic crowdsourcing in the future. We begin by defining VGI, CS, PM and crowdsourcing. Two typologies are then offered as methods to conceptualize these practices and the Kitchin (2014) data assemblage framework is proposed as a method by which state actors can critically examine their data infrastructures. A selection of exemplary VGI, CS and PM from Canada and the Republic of Ireland are discussed and the paper concludes with some high level recommendations for administrations considering a geographic approach to crowdsourcing