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

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Spectators’ aesthetic experiences of sound and movement in dance performance

In this paper we present a study of spectators’ aesthetic experiences of sound and movement in live dance performance. A multidisciplinary team comprising a choreographer, neuroscientists and qualitative researchers investigated the effects of different sound scores on dance spectators. What would be the impact of auditory stimulation on kinesthetic experience and/or aesthetic appreciation of the dance? What would be the effect of removing music altogether, so that spectators watched dance while hearing only the performers’ breathing and footfalls? We investigated audience experience through qualitative research, using post-performance focus groups, while a separately conducted functional brain imaging (fMRI) study measured the synchrony in brain activity across spectators when they watched dance with sound or breathing only. When audiences watched dance accompanied by music the fMRI data revealed evidence of greater intersubject synchronisation in a brain region consistent with complex auditory processing. The audience research found that some spectators derived pleasure from finding convergences between two complex stimuli (dance and music). The removal of music and the resulting audibility of the performers’ breathing had a significant impact on spectators’ aesthetic experience. The fMRI analysis showed increased synchronisation among observers, suggesting greater influence of the body when interpreting the dance stimuli. The audience research found evidence of similar corporeally focused experience. The paper discusses possible connections between the findings of our different approaches, and considers the implications of this study for interdisciplinary research collaborations between arts and sciences

The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin

The TREC-2002 video track report

TREC-2002 saw the second running of the Video Track, the goal of which was to promote progress in content-based retrieval from digital video via open, metrics-based evaluation. The track used 73.3 hours of publicly available digital video (in MPEG-1/VCD format) downloaded by the participants directly from the Internet Archive (Prelinger Archives) (internetarchive, 2002) and some from the Open Video Project (Marchionini, 2001). The material comprised advertising, educational, industrial, and amateur films produced between the 1930's and the 1970's by corporations, nonprofit organizations, trade associations, community and interest groups, educational institutions, and individuals. 17 teams representing 5 companies and 12 universities - 4 from Asia, 9 from Europe, and 4 from the US - participated in one or more of three tasks in the 2001 video track: shot boundary determination, feature extraction, and search (manual or interactive). Results were scored by NIST using manually created truth data for shot boundary determination and manual assessment of feature extraction and search results. This paper is an introduction to, and an overview of, the track framework - the tasks, data, and measures - the approaches taken by the participating groups, the results, and issues regrading the evaluation. For detailed information about the approaches and results, the reader should see the various site reports in the final workshop proceedings

The TRECVID 2007 BBC rushes summarization evaluation pilot

This paper provides an overview of a pilot evaluation of video summaries using rushes from several BBC dramatic series. It was carried out under the auspices of TRECVID. Twenty-two research teams submitted video summaries of up to 4% duration, of 42 individual rushes video files aimed at compressing out redundant and insignificant material. The output of two baseline systems built on straightforward content reduction techniques was contributed by Carnegie Mellon University as a control. Procedures for developing ground truth lists of important segments from each video were developed at Dublin City University and applied to the BBC video. At NIST each summary was judged by three humans with respect to how much of the ground truth was included, how easy the summary was to understand, and how much repeated material the summary contained. Additional objective measures included: how long it took the system to create the summary, how long it took the assessor to judge it against the ground truth, and what the summary's duration was. Assessor agreement on finding desired segments averaged 78% and results indicate that while it is difficult to exceed the performance of baselines, a few systems did

PhD

dissertationThe purpose of this study is to develop a critical perspective which can be used to address the social reality of nursing. The study uses the tradition of the Frankfurt School and critical theory as the primary intellectual frameworks for developing this perspective. The investigation is primarily an exploratory, reflective study which seeks to develop a critical consciousness about nursing. The methodology used in this study differs significantly from empirical-analytic modes of inquiry. The investigation proceeds via the process of reflection. Radical reflection, the method used in this study, contains five stages or steps. They include: bracketing, historical recovery, critique, dialectical imagination and negotiation. The study proceeds in an exploratory way through each of these steps. As in other forms of reflection, findings produced in this study take the form of hypotheses. The study generates insights or interpretive hypotheses about the social construction of reality in nursing. As in other examples of reflection, these are hypotheses whose confirmation depends upon continued negotiation among nurses. Specific findings generated in this study include 1) a critique of scientistic consciousness in nursing, 2) a critique of bourgeois professional ideology in nursing and 3) a critique of sexism in nursing

The arts of action

The theory and culture of the arts has largely focused on the arts of objects, and neglected the arts of action – the “process arts”. In the process arts, artists create artifacts to engender activity in their audience, for the sake of the audience’s aesthetic appreciation of their own activity. This includes appreciating their own deliberations, choices, reactions, and movements. The process arts include games, urban planning, improvised social dance, cooking, and social food rituals. In the traditional object arts, the central aesthetic properties occur in the artistic artifact itself. It is the painting that is beautiful; the novel that is dramatic. In the process arts, the aesthetic properties occur in the activity of the appreciator. It is the game player’s own decisions that are elegant, the rock climber’s own movement that is graceful, and the tango dancers’ rapport that is beautiful. The artifact’s role is to call forth and shape that activity, guiding it along aesthetic lines. I offer a theory of the process arts. Crucially, we must distinguish between the designed artifact and the prescribed focus of aesthetic appreciation. In the object arts, these are one and the same. The designed artifact is the painting, which is also the prescribed focus of appreciation. In the process arts, they are different. The designed artifact is the game, but the appreciator is prescribed to appreciate their own activity in playing the game. Next, I address the complex question of who the artist really is in a piece of process art — the designer or the active appreciator? Finally, I diagnose the lowly status of the process arts

Multiple view human activity recognition

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Ph. D.) -- Bilkent University, 2012.Includes bibliographical references leaves 94-100.This thesis explores the human activity recognition problem when multiple views are available. We follow two main directions: we first present a system that performs volume matching using constructed 3D volumes from calibrated cameras, then we present a flexible system based on frame matching directly using multiple views. We examine the multiple view systems compared to single view systems, and measure the performance improvements in recognition using more views by various experiments. Initial part of the thesis introduces compact representations for volumetric data gained through reconstruction. The video frames recorded by many cameras with significant overlap are fused by reconstruction, and the reconstructed volumes are used as substitutes of action poses. We propose new pose descriptors over these three dimensional volumes. Our first descriptor is based on the histogram of oriented cylinders in various sizes and orientations. We then propose another descriptor which is view-independent, and which does not require pose alignment. We show the importance of discriminative pose representations within simpler activity classification schemes. Activity recognition framework based on volume matching presents promising results compared to the state-of-the-art. Volume reconstruction is one natural approach for multi camera data fusion, but there can be few cameras with overlapping views. In the second part of the thesis, we introduce an architecture that is adaptable to various number of cameras and features. The system collects and fuses activity judgments from cameras using a voting scheme. The architecture requires no camera calibration. Performance generally improves when there are more cameras and more features; training and test cameras do not need to overlap; camera drop in or drop out is handled easily with little penalty. Experiments support the performance penalties, and advantages for using multiple views versus single view.Pehlivan, SelenPh.D