SALSA: A Novel Dataset for Multimodal Group Behavior Analysis

Studying free-standing conversational groups (FCGs) in unstructured social settings (e.g., cocktail party ) is gratifying due to the wealth of information available at the group (mining social networks) and individual (recognizing native behavioral and personality traits) levels. However, analyzing social scenes involving FCGs is also highly challenging due to the difficulty in extracting behavioral cues such as target locations, their speaking activity and head/body pose due to crowdedness and presence of extreme occlusions. To this end, we propose SALSA, a novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis, and make two main contributions to research on automated social interaction analysis: (1) SALSA records social interactions among 18 participants in a natural, indoor environment for over 60 minutes, under the poster presentation and cocktail party contexts presenting difficulties in the form of low-resolution images, lighting variations, numerous occlusions, reverberations and interfering sound sources; (2) To alleviate these problems we facilitate multimodal analysis by recording the social interplay using four static surveillance cameras and sociometric badges worn by each participant, comprising the microphone, accelerometer, bluetooth and infrared sensors. In addition to raw data, we also provide annotations concerning individuals' personality as well as their position, head, body orientation and F-formation information over the entire event duration. Through extensive experiments with state-of-the-art approaches, we show (a) the limitations of current methods and (b) how the recorded multiple cues synergetically aid automatic analysis of social interactions. SALSA is available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure

Factors influencing visual attention switch in multi-display user interfaces: a survey

Multi-display User Interfaces (MDUIs) enable people to take advantage of the different characteristics of different display categories. For example, combining mobile and large displays within the same system enables users to interact with user interface elements locally while simultaneously having a large display space to show data. Although there is a large potential gain in performance and comfort, there is at least one main drawback that can override the benefits of MDUIs: the visual and physical separation between displays requires that users perform visual attention switches between displays. In this paper, we present a survey and analysis of existing data and classifications to identify factors that can affect visual attention switch in MDUIs. Our analysis and taxonomy bring attention to the often ignored implications of visual attention switch and collect existing evidence to facilitate research and implementation of effective MDUIs.Postprin