A camera control based on fuzzy behavior recognition of lecturer for distance lecture

Tsuruoka, Shuichi; Yamaguchi, Tomoharu; Kato, Keizo; Yoshikawa, Tomohiro; Shinogi, Tsuyoshi

doi:10.1109/fuzz.2001.1009111

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…The advantage of SDCC is that the 3D representation permits more flexible and reliable handling of complex appearance, disappearance, trajectory modelling and other target's interactions with the environment. Some examples are Tsuruoka et al [15], who present a system that tracks a lecturer in a classroom using foreground segmentation on a fixed camera's image and a fuzzy control scheme to steer an active camera for close-up views. Another example is the "Face Cataloger" application presented by Hampapur et al [16], which uses multiple active and passive cameras.…”

Section: Camera Control Strategiesmentioning

confidence: 99%

Cognitive visual tracking and camera control

Bellotto

Benfold

Harland

et al. 2012

Computer Vision and Image Understanding

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Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision.

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Section: Camera Control Strategiesmentioning

confidence: 99%

Cognitive visual tracking and camera control

Bellotto

Benfold

Harland

et al. 2012

Computer Vision and Image Understanding

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show abstract

“…Tsuruoka et al [11] present a system that tracks a lecturer in a classroom using foreground segmentation on images from a fixed camera and uses a fuzzy control scheme to steer an active camera and deliver closeup views. It is however limited to a single user standing in front of a clean background.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Audio-visual multi-person tracking and identification for smart environments

Bernardin

Stiefelhagen

2007

Proceedings of the 15th ACM International Conference on Multimedia

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This paper presents a novel system for the automatic and unobtrusive tracking and identification of multiple persons in an indoor environment. Information from several fixed cameras is fused in a particle filter framework to simultaneously track multiple occupants. A set of steerable fuzzycontrolled pan-tilt-zoom cameras serves to smoothly track persons of interest and opportunistically capture facial closeups for face identification. In parallel, speech segmentation, sound source localization and speaker identification are performed using several far-field microphones and arrays. The information coming asynchronously and sporadically from several sources, such as track updates and spatio-temporally localized visual and acoustic identification cues, is fused at higher level to gradually refine the global scene model and increase the system's confidence in the set of recognized identities. The system has been trained on a small set of users' faces and/or voices and showed good performance in natural meeting scenarios at quickly acquiring their identities and complementing the ID information missing in single modalities.

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“…Tsuruoka et al [10] used a steerable camera to make more appealing recordings of presentations available to remote viewers. In addition to the active camera, which is used only for recording, a static camera is used for all image processing, target position computation, and control command generation.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Automatic Person Detection and Tracking using Fuzzy Controlled Active Cameras

Bernardin

Camp

Stiefelhagen

2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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This paper presents an automatic system for the monitoring of indoor environments using pan-tilt-zoomable cameras. A combination of Haar-feature classifier-based detection and color histogram filtering is used to achieve reliable initialization of person tracks even in the presence of camera movement. A combination of adaptive color and KLT feature trackers for face and upper body allows for robust tracking and track recovery in the presence of occlusion or interference. The continuous recomputation of camera parameters, coupled with a fuzzy controlling scheme allow for smooth tracking of moving targets as well as acquisition of stable facial closeups, similar to the natural behavior of a human cameraman. The system is tested on a series of natural indoor monitoring scenarios and shows a high degree of naturalness, flexibility and robustness.

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A camera control based on fuzzy behavior recognition of lecturer for distance lecture

Cited by 7 publications

References 3 publications

Cognitive visual tracking and camera control

Cognitive visual tracking and camera control

Audio-visual multi-person tracking and identification for smart environments

Automatic Person Detection and Tracking using Fuzzy Controlled Active Cameras

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