Active Camera Networks and Semantic Event Databases for Intelligent Environments

Trivedi, Mohan M.; MikiÆ, Ivana; Bhonsle, S.

doi:10.1080/03772063.2002.11416289

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…Others [15,12] can work with non-overlapping cameras but still require calibration. Probabilistic approaches have been presented [8,22], however these are often limited in application due to restrictive assumptions. KaewTraKulPong and Bowden [13] or Ellis et al [9] do not require a priori correspondences to be explicitly stated, instead they use the observed motion over time to establish reappearance periods.…”

Section: Related Workmentioning

confidence: 99%

Accurate fusion of robot, camera and wireless sensors for surveillance applications

Gilbert

Illingworth

Bowden

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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Often within the field of tracking people only fixed cameras are used. This can mean that when the the illumination of the image changes or object occlusion occurs, the tracking can fail. We propose an approach that uses three simultaneous separate sensors. The fixed surveillance cameras track objects of interest cross camera through incrementally learning relationships between regions on the image. Cameras and laser rangefinder sensors onboard robots also provide an estimate of the person. Moreover, the signal strength of mobile devices carried by the person can be used to estimate his position. The estimate from all these sources are then combined using data fusion to provide an increase in performance. We present results of the fixed camera based tracking operating in real time on a large outdoor environment of over 20 non-overlapping cameras. Moreover, the tracking algorithms for robots and wireless nodes are described. A decentralized data fusion algorithm for combining all these information is presented.

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Section: Related Workmentioning

confidence: 99%

Accurate fusion of robot, camera and wireless sensors for surveillance applications

Gilbert

Illingworth

Bowden

2009

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops

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“…Target tracking in image sequences has widespread applications, particularly in areas of intelligent robotics [1], [2], visual surveillance [3], human-computer interaction [4], [5] and biomedical image analysis [6]. Robust real-time tracking target with unconstrained movement in moving monocular camera image sequence is extremely challenging due to these reasons such as moving camera, non-stationary target motion, lighting conditions and cluttered background.…”

Section: Introductionmentioning

confidence: 99%

Real-time target tracking with particle filter in moving monocular camera

Liu

Wang

2007

SPIE Proceedings

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In this paper, we propose an improved particle filter algorithm for real-time tracking a randomly moving target in dynamic environment with a moving monocular camera. For making the tracking task robustly and effectively, color histogram based target model is integrated into particle filter algorithm. Bhattacharyya distance is used to weight samples by calculating each sample's histogram with a specified target model and it makes the measurement matching and samples' weight updating more reasonable. In order to reduce sample depletion, the improved algorithm will be able to take the latest observation into account. The experimental results confirm that the method is effective even when the monocular camera is moving and the target object is partially occluded in a clutter background.

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“…At a higher level, results of 3-D tracking, voice recognition, person identification (which is itself achieved using multimodal information) and knowledge of the structure of the environment are used to recognize interesting events. When a person enters the room, the system takes the snapshot of their face and sample of their speech to perform person identification using face and voice recognition [35], [36].…”

Section: Human Activity and Interactions In An Intelligent Meetingmentioning

confidence: 99%

Dynamic Context Capture and Distributed Video Arrays for Intelligent Spaces

Trivedi

Huang

Mikić

2005

IEEE Trans. Syst., Man, Cybern. A

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Abstract-Intelligent environments can be viewed as systems where humans and machines (rooms) collaborate. Intelligent (or smart) environments need to extract and maintain an awareness of a wide range of events and human activities occurring in these spaces. This requirement is crucial for supporting efficient and effective interactions among humans as well as humans and intelligent spaces. Visual information plays an important role for developing accurate and useful representation of the static and dynamic states of an intelligent environment. Accurate and efficient capture, analysis, and summarization of the dynamic context requires the vision system to work at multiple levels of semantic abstractions in a robust manner. In this paper, we present details of a long-term and ongoing research project, where indoor intelligent spaces endowed with a range of useful functionalities are designed, built, and systematically evaluated. Some of the key functionalities include: intruder detection; multiple person tracking; body pose and posture analysis; person identification; human body modeling and movement analysis; and for integrated systems for intelligent meeting rooms, teleconferencing, or performance spaces. The paper includes an overall system architecture to support design and development of intelligent environments. Details of panoramic (omnidirectional) video camera arrays, calibration, video stream synchronization, and real-time capture/processing are discussed. Modules for multicamera-based multiperson tracking, event detection and event based servoing for selective attention, voxelization, streaming face recognition, are also discussed. The paper includes experimental studies to systematically evaluate performance of individual video analysis modules as well as to evaluate basic feasibility of an integrated system for dynamic context capture and event based servoing, and semantic information summarization.

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Active Camera Networks and Semantic Event Databases for Intelligent Environments

Cited by 17 publications

References 13 publications

Accurate fusion of robot, camera and wireless sensors for surveillance applications

Accurate fusion of robot, camera and wireless sensors for surveillance applications

Real-time target tracking with particle filter in moving monocular camera

Dynamic Context Capture and Distributed Video Arrays for Intelligent Spaces

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