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

External localization system for mobile robotics

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the proposed localization system is an efficient method for black and white circular pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision, and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost camera, its core algorithm is able to process hundreds of images per second while tracking hundreds of objects with millimeter precision. We propose a mathematical model of the method that allows to calculate its precision, area of coverage, and processing speed from the camera’s intrinsic parameters and hardware’s processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions are verified in several experiments. Apart from the method description, we also publish its source code; so, it can be used as an enabling technology for various mobile robotics problems