Automating camera management for lecture room environments

Liu, Qiong; Rui, Yong; Gupta, Anoop; Cadiz, JJ

doi:10.1145/365024.365310

Cited by 87 publications

(78 citation statements)

References 12 publications

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“…In general, face pose may present human physical states such as sleeping and concentration. As a result, it is very important in many real-life applications, such as monitoring attentiveness of drivers or automating camera management [8].…”

Section: Face Detection Systemmentioning

confidence: 99%

Designing Fuzzy Inference System to Diagnosis down syndrome by Face Processing

Keikhayfarzaneh¹,

Mousavi²,

Khalatbari³

2011

IJCA

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Various kind of mental illness is common in all parts of the globe, and all age groups. New diagnostic methods will be vital stage in reducing or eliminating these types of disease. In this paper novel fuzzy rule-base system is designed, and programmed by computer software, for diagnosis of Down Syndrom faces in an image. System input is an arbitrary color image. In first step face regions should be selected. An accurate face detection system is utilized which applies skin color, lip position, face shape information and ear texture properties, as the key parameters. After this stage, detected face regions are processing carefully by proposed fuzzy system, and some features such as face area and eye distance are investigating carefully. Finally the probability of being Down Syndrom is revealed by designed system. 98.33% correct detection is obtained applying this algorithm on various image databases. This system could be considered as the first step to device automatic system for diagnosis of illness and would help psychiatry.

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Section: Face Detection Systemmentioning

confidence: 99%

Designing Fuzzy Inference System to Diagnosis down syndrome by Face Processing

Keikhayfarzaneh¹,

Mousavi²,

Khalatbari³

2011

IJCA

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“…Without multiple views, users may lack the visual information required to understand the context [7]. Further, a relatively static camera typically results in a video that is boring to watch [11,15]. Having dedicated video production staff does improve the video, but this comes at a significant cost that would likely be prohibitive for most meetings.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, there has been significant recent interest in automating camera control to make videos that look more like those shot by professionals. This is accomplished by understanding what the camera should be aimed at [21,24,31], how it should be moved [3,14] and when to cut between cameras [11,15,28]. Professional crews notice and respond to cues such as who is speaking, who is likely to speak next, gestures and other body language, and a set of heuristics about when to cut between shots [2,38].…”

Section: Introductionmentioning

confidence: 99%

Improving meeting capture by applying television production principles with audio and motion detection

Ranjan

Birnholtz

Balakrishnan

2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Video recordings of meetings are often monotonous and tedious to watch. In this paper, we report on the design, implementation and evaluation of an automated meeting capture system that applies television production principles to capture and present videos of small group meetings in a compelling manner. The system uses inputs from a motion capture system and microphones to drive multiple pan-tiltzoom cameras and uses heuristics to frame shots and cut between them. An evaluation of the system indicates that its performance approaches that of a professional crew while requiring significantly fewer human resources.

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“…In general, face pose may represent human physical state, such as sleepiness and concentration. Therefore, it is very important in many real-life applications, such as monitoring attentiveness of drivers [3] or automating camera management [4]. In addition, many view-based approaches for face image analysis such as face recognition usually need to estimate the pose to some extent [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-view face pose classification by tree-structured classifier

Yang

Okamoto

et al. 2005

IEEE International Conference on Image Processing 2005

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In this paper, a multi-view face pose classification method is introduced. Each face region is normalized by two eye-centers and mouth center, and then multi-class classifiers are trained for face pose classification. The face pose classification is organized into a tree structure that deals with off-image plane pose variation. As a specific application in video, it can estimate the face pose in each frame that results in a face pose variation trajectory, which may have important applications such as in security monitoring of car drivers. Experiment results on a very large set compared with a PCA reconstruction method as a benchmark are reported to show its effectiveness.

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Automating camera management for lecture room environments

Cited by 87 publications

References 12 publications

Designing Fuzzy Inference System to Diagnosis down syndrome by Face Processing

Designing Fuzzy Inference System to Diagnosis down syndrome by Face Processing

Improving meeting capture by applying television production principles with audio and motion detection

Multi-view face pose classification by tree-structured classifier

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