Discriminatively Trained Latent Ordinal Model for Video Classification

We study the problem of video classification for facial analysis and human action recognition. We propose a novel weakly supervised learning method that models the video as a sequence of automatically mined, discriminative sub-events (eg. onset and offset phase for "smile", running and jumping for "highjump"). The proposed model is inspired by the recent works on Multiple Instance Learning and latent SVM/HCRF -- it extends such frameworks to model the ordinal aspect in the videos, approximately. We obtain consistent improvements over relevant competitive baselines on four challenging and publicly available video based facial analysis datasets for prediction of expression, clinical pain and intent in dyadic conversations and on three challenging human action datasets. We also validate the method with qualitative results and show that they largely support the intuitions behind the method.Comment: Paper accepted in IEEE TPAMI. arXiv admin note: substantial text overlap with arXiv:1604.0150

Descriptive temporal template features for visual motion recognition

In this paper, a human action recognition system is proposed. The system is based on new, descriptive `temporal template' features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well known `Motion History Image' (MHI) temporal template are addressed and a new `Motion History Histogram' (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations

RGB-D-based Action Recognition Datasets: A Survey

Human action recognition from RGB-D (Red, Green, Blue and Depth) data has attracted increasing attention since the first work reported in 2010. Over this period, many benchmark datasets have been created to facilitate the development and evaluation of new algorithms. This raises the question of which dataset to select and how to use it in providing a fair and objective comparative evaluation against state-of-the-art methods. To address this issue, this paper provides a comprehensive review of the most commonly used action recognition related RGB-D video datasets, including 27 single-view datasets, 10 multi-view datasets, and 7 multi-person datasets. The detailed information and analysis of these datasets is a useful resource in guiding insightful selection of datasets for future research. In addition, the issues with current algorithm evaluation vis-\'{a}-vis limitations of the available datasets and evaluation protocols are also highlighted; resulting in a number of recommendations for collection of new datasets and use of evaluation protocols

Contribution of simulation and gaming to natural resource management issues: An introduction

Nowadays, computer-mediated simulations and games are widely used in the field of natural resource management (NRM). They have proved to be useful for various purposes such as supporting decisionmaking processes and training. First, the specificities of the NRM research field are highlighted. Then, based on the analysis of the articles presented in this special issue of Simulation & Gaming, some key features related to the implementation of gaming in such a context are introduced. Finally, after reviewing the benefits of using simulation games in NRM, the authors stress the ethical issue of changing social relationships among stakeholders by playing a game with some of themGESTION DE L'ENVIRONNEMENT;RESSOURCE NATURELLE;SIMULATION;SOCIOLOGIE;JEU DE ROLE;BENEFITS;CONTEXT;COLLECTIVE POLICY DESIGN;DECISION MAKING;ETHICAL ISSUES;IMPLEMENTATION;NATURAL RESOURCE MANAGEMENT (NRM);SIMULATION GAMES;SOCIAL EMPOWERMENT;SOCIAL RELATIONSHIPS;SOCIOECOLOGICAL SYSTEMS;STAKEHOLDERS

DESIGNING EDUCATIONAL PROGRAMS ON PUBLIC ISSUES

Public Economics,

Historical Consciousness and Ethnicity: How Signifying the Past Influences the Fluctuations in Ethnic Boundary Maintenance

Theorists tend to limit \u27history\u27s\u27 role in the dynamics of ethnicity to that generally played by collective memory. By bringing the notion of historical consciousness to the fore, new possibilities may, however, emerge for discerning how history, as one cultural mode of remembering among many others, impacts both ethnicity delineations and fluctuations in boundary maintenance. In encapsulating the many forms of commemoration as well as the different dimensions of historical thinking, the contribution of historical consciousness accordingly lies on how group members historicize temporal change for moral orientation in time. By likewise signifying past events for negotiating their ethnicity and agency toward the \u27significant Other\u27, social actors gate-keep group boundaries. And, depending on their capacity and willingness to recognize the \u27significant Other\u27s\u27 moral and historical agency in the flow of time, they can transform group delineations and render ethnic boundaries more porous. Key Words: Historical Consciousness; Ethnicity; Group Boundaries; Boundary Maintenance; Boundary Fluctuations; Collective Memory; Disciplinary History; Moral and Historical Agency