Monitoring, recognizing and discovering social networks

Yu, Tao; Lim, Ser-Nam; Patwardhan, Kedar A.; Krahnstoever, N.

doi:10.1109/cvpr.2009.5206526

Cited by 63 publications

(58 citation statements)

References 18 publications

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“…In contrast, we extend the state of the art as follows: Opposed to [9,7,20,4,14,11,16] which rely on static overhead cameras to perceive the scene, we address the problem of social grouping and tracking from a mobile sensor and a first-person perspective. Overhead cameras are sufficient for surveillance but fall short of scenarios where a robot, an intelligent vehicle or an interactive system coexists and acts in the same space with people.…”

Section: Related Workmentioning

confidence: 99%

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Multi-Hypothesis Social Grouping and Tracking for Mobile Robots

Luber¹,

Arras²

2013

Robotics: Science and Systems IX

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Abstract-Detecting and tracking people and groups of people is a key skill for robots in populated environments. In this paper, we address the problem of detecting and learning socio-spatial relations between individuals and to track their group formations. Opposed to related work, we track and reason about multiple social grouping hypotheses in a recursive way, assume a mobile sensor that perceives the scene from a first-person perspective, and achieve good tracking performance in real-time using only 2D range data. The method, that relies on an extended multi-hypothesis tracking approach, also improves person-level tracking in two ways: the social grouping information is fed back to predict human motion over learned intra-group constraints and to support data association by adapting track-specific occlusion probabilities. Both measures lead to an improved occlusion handling and a better trade-off between false negative and false positive tracks. In experiments with a mobile robot and on large-scale outdoor data sets, we demonstrate how the approach is able to model social grouping and to improve person tracking by a significant reduction of track identifier switches and false negative tracks.

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Section: Related Workmentioning

confidence: 99%

“…Yu et al [20] address the problem of discovery and analysis of social networks from individuals tracked in surveillance videos. A social network graph is built over time from observations of interacting individuals.…”

Section: Related Workmentioning

confidence: 99%

Multi-Hypothesis Social Grouping and Tracking for Mobile Robots

Luber¹,

Arras²

2013

Robotics: Science and Systems IX

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“…In [92], the authors propose a system that could track and discover groups of interacting people, estimating the trajectories of people and employing the modularity cut algorithm [93]. A limitation of the work was that of considering solely staged social activities.…”

Section: Space and Environmentmentioning

confidence: 99%

Human behavior analysis in video surveillance: A Social Signal Processing perspective

et al. 2013

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The analysis of human activities is one of the most intriguing and important open issues for the automated video surveillance community. Since few years ago, it has been handled following a mere Computer Vision and Pattern Recognition perspective, where an activity corresponded to a temporal sequence of explicit actions (run, stop, sit, walk, etc.). Even under this simplistic assumption, the issue is hard, due to the strong diversity of the people appearance, the number of individuals considered (we may monitor single individuals, groups, crowd), the variability of the environmental conditions (indoor/outdoor, different weather conditions), and the kinds of sensors employed. More recently, the automated surveillance of human activities has been faced considering a new perspective, that brings in notions and principles from the social, affective, and psychological literature, and that is called Social Signal Processing (SSP). SSP employs primarily nonverbal cues, most of them are outside of conscious awareness, like face expressions and gazing, body posture and gestures, vocal characteristics, relative distances in the space and the like. This paper is the first review analyzing this new trend, proposing a structured snapshot of the state of the art and envisaging novel challenges in the surveillance domain where the cross-pollination of Computer Science technologies and Sociology theories may offer valid investigation strategies.

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“…Suppose, social connection strength is between two persons, and . To establish strength of tie between individuals and the following assumptions are required [8,38].…”

Section: Social Networkmentioning

confidence: 99%

“…Social network analysis is used to understand the pattern of interaction caused by social dynamics and events [8,9,10,11,12,13]. One special kind of network is known as social network and has been studying for long time [3,4,5,6].…”

Section: Social Networkmentioning

confidence: 99%

Link Mining: A Computer Vision and Pattern Mining Approach

Mishra¹,

Nandi²

2013

IJCA

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This work addresses the important problem of discovery and analysis of social networks and link between frequent people observed from surveillance video footage where large amount of video data is collected routinely. A computer vision approach has been applied for detecting and extracting people within a group using HAAR classifier. This technique allows us to recognizing people by doing similarity matching between training faces and unknown detected face image. Therefore it is required to obtain high resolution face images of people in order to extract intrinsic feature information of facial images used in detection of person's faces. We present a novel frequent pattern mining based approach in the domain of frequent person detection i.e. apriori to solve frequent association problem between social networks obtained from low level task of face recognition. Our approach is illustrated with promising results from a fully integrated camera system.

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Monitoring, recognizing and discovering social networks

Cited by 63 publications

References 18 publications

Multi-Hypothesis Social Grouping and Tracking for Mobile Robots

Multi-Hypothesis Social Grouping and Tracking for Mobile Robots

Human behavior analysis in video surveillance: A Social Signal Processing perspective

Link Mining: A Computer Vision and Pattern Mining Approach

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