Modeling Dominance in Group Conversations using NonVerbal Activity Cues

Dominance - a behavioral expression of power - is a fundamental mechanism of social interaction, expressed and perceived in conversations through spoken words and audio-visual nonverbal cues. The automatic modeling of dominance patterns from sensor data represents a relevant problem in social computing. In this paper, we present a systematic study on dominance modeling in group meetings from fully automatic nonverbal activity cues, in a multi-camera, multi-microphone setting. We investigate efficient audio and visual activity cues for the characterization of dominant behavior, analyzing single and joint modalities. Unsupervised and supervised approaches for dominance modeling are also investigated. Activity cues and models are objectively evaluated on a set of dominance-related classification tasks, derived from an analysis of the variability of human judgment of perceived dominance in group discussions. Our investigation highlights the power of relatively simple yet efficient approaches and the challenges of audio-visual integration. This constitutes the most detailed study on automatic dominance modeling in meetings to date

Predicting the Dominant Clique in Meetings through Fusion of Nonverbal Cues

This paper addresses the problem of automatically predicting the dominant clique (i.e., the set of K-dominant people) in face-to-face small group meetings recorded by multiple audio and video sensors. For this goal, we present a framework that integrates automatically extracted nonverbal cues and dominance prediction models. Easily computable audio and visual activity cues are automatically extracted from cameras and microphones. Such nonverbal cues, correlated to human display and perception of dominance, are well documented in the social psychology literature. The effectiveness of the cues were systematically investigated as single cues as well as in unimodal and multimodal combinations using unsupervised and supervised learning approaches for dominant clique estimation. Our framework was evaluated on a five-hour public corpus of teamwork meetings with third-party manual annotation of perceived dominance. Our best approaches can exactly predict the dominant clique with 80.8% accuracy in four-person meetings in which multiple human annotators agree on their judgments of perceived dominance

CITRIC: A low-bandwidth wireless camera network platform

In this paper, we propose and demonstrate a novel wireless camera network system, called CITRIC. The core component of this system is a new hardware platform that integrates a camera, a frequency-scalable (up to 624 MHz) CPU, 16 MB FLASH, and 64 MB RAM onto a single device. The device then connects with a standard sensor network mote to form a camera mote. The design enables in-network processing of images to reduce communication requirements, which has traditionally been high in existing camera networks with centralized processing. We also propose a back-end client/server architecture to provide a user interface to the system and support further centralized processing for higher-level applications. Our camera mote enables a wider variety of distributed pattern recognition applications than traditional platforms because it provides more computing power and tighter integration of physical components while still consuming relatively little power. Furthermore, the mote easily integrates with existing low-bandwidth sensor networks because it can communicate over the IEEE 802.15.4 protocol with other sensor network platforms. We demonstrate our system on three applications: image compression, target tracking, and camera localization

Using Audio and Video Features to Classify the Most Dominant Person in a Group Meeting

The automated extraction of semantically meaningful information from multi-modal data is becoming increasingly necessary due to the escalation of captured data for archival. A novel area of multi-modal data labelling, which has received relatively little attention, is the automatic estimation of the most dominant person in a group meeting. In this paper, we provide a framework for detecting dominance in group meetings using different audio and video cues. We show that by using a simple model for dominance estimation we can obtain promising results

Digital Television Format Conversions

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On Compression of Encrypted Video

We consider video sequences that have been encrypted uncompressed. Since encryption masks the source, traditional data compression algorithms are rendered ineffective. However, it has been shown that through the use of distributed source-coding techniques, the compression of encrypted data is in fact possible. This means that it is possible to reduce data size without requiring that the data be compressed prior to encryption. Indeed, under some reasonable conditions, neither security nor compression efficiency need be sacrificed when compression is performed on the encrypted data. In this paper we develop an algorithm for the practical lossless compression of encrypted gray scale video. Our method is based on considering the temporal correlations in video. This move to temporal dependence builds on our previous work on memoryless sources, and on- and two-dimensional Markov sources. For comparison, a motion-compensated lossless video encoder can compress each unencrypted frame of the standard ¨Foreman¨test video sequence by about 57%. Our algorithm can compress the same frames, after encryption, by about 33

An investigation of methods for digital television format conversions

Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 75-76).by Chuohao Yeo.M.Eng.and S.B