Human tracking using distributed vision systems

Nakazawa, Atsushi; Kato, Hiroharu; Ishikawa, Seiji

doi:10.1109/icpr.1998.711213

Cited by 51 publications

(40 citation statements)

References 4 publications

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“…The first three assumptions related to movements are very general and used in every system with a few exceptions; see, e.g., [19,36,105]. The fourth assumption is mainly used in human computer interaction (HCI) applications and simplifies the calculation of the overall body pose.…”

Section: Assumptionsmentioning

confidence: 99%

“…The use of background subtraction is very popular. If the scene is static, a noise-free background image without any subject(s) may be recorded and used as a reference in a subtraction scheme [105]. A more advanced version is to update the background image during processing [53].…”

Section: Temporal Datamentioning

confidence: 99%

“…The philosophy is to represent the subject by a set of boundary boxes containing the pixels or regions found in the figureground segmentation process. Some systems track these boxes over time [105], while others use them as intermediate representations [142], which are processed (rerepresented) further in pose estimation.…”

Section: Object-based Representationmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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Section: Assumptionsmentioning

confidence: 99%

Section: Temporal Datamentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Computer Vision-Based Human Motion Capture

Moeslund¹,

Granum²

2001

Computer Vision and Image Understanding

1,422

642

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“…Research on multi-sensor network devoted to human tracking and identification can be found in [4], [13], [12], [14] and [15]. An integrated system of active camera network has been proposed in [16] for human tracking and face recognition.…”

Section: Related Workmentioning

confidence: 99%

A Fault-Tolerant Distributed Vision System Architecture for Object Tracking in a Smart Room

Karuppiah¹,

Zhu²,

Shenoy³

et al. 2001

Lecture Notes in Computer Science

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Abstract. In recent years, distributed computer vision has gained a lot of attention within the computer vision community for applications such as video surveillance and object tracking. The collective information gathered by multiple cameras that are strategically placed has many advantages. For example, aggregation of information from multiple viewpoints reduces the uncertainty about the scene. Further, there is no single point of failure, thus the system as a whole could continue to perform the task at hand. However, the advantages arising out of such cooperation can be realized only by timely sharing of the information between them. This paper discusses the design of a distributed vision system that enables several heterogeneous sensors with different processing rates to exchange information in a timely manner in order to achieve a common goal, say tracking of multiple human subjects and mobile robots in an indoor smart environment. In our fault-tolerant distributed vision system, a resource manager manages individual cameras and buffers the time-stamped object candidates received from them. A User Agent with a given task specification approaches the resource manager, first for knowing the available resources (cameras) and later for receiving the object candidates from the resources of its interest. Thus the resource manager acts as a proxy between the user agents and cameras, thereby freeing the cameras to do dedicated feature detection and extraction only. In such a scenario, many failures are possible. For example, one of the cameras may have a hardware failure or it may lose the target, which moved away from its field of view. In this context, important issues such as failure detection and handling, synchronization of data from multiple sensors and sensor reconfiguration by view planning are discussed in the paper. Experimental results with real scene images will be given.

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“…Therefore, the challenge will be to provide sensor data for each of the modules by coupling the modules together in a sensor perception/action cycle (Nakazawa et al 2002;Ukita and Matsuyama 2005). These cooperating pan-tilt-zoom sensors involved in acquisition will also serve the purpose of providing sensor data for each of the modules, but more importantly couple the other workpackages together in a sensor perception/action cycle.…”

Section: Innovation Brought By Hse Researchmentioning

confidence: 99%

Research Steps Towards Human Sequence Evaluation

Gonzàlez

Roca²,

Villanueva³

Advances in Computational Vision and Medical Image Processing

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Human Sequence Evaluation (HSE) concentrates on how to extract descriptions of human behaviour from videos in a restricted discourse domain, such as (i) pedestrians crossing inner-city roads where pedestrians appear approaching or waiting at stops of busses or trams, and (ii) humans in indoor worlds like an airport hall, a train station, or a lobby. These discourse domains allow exploring a coherent evaluation of human movements and facial expressions across a wide variation of scale. This general approach lends itself to various cognitive surveillance scenarios at varying degrees of resolution: from wide-field-of-view multiple-agent scenes, through to more specific inferences of emotional state that could be elicited from high resolution imagery of faces. The true challenge of the HERMES project will consist in the development of a system facility which starts with basic knowledge about pedestrian behaviour in the chosen discourse domain, but could cluster evaluation results into semantically meaningful subsets of behaviours. The envisaged system will comprise an internal logic-based representation which enables it to comment each individual subset, giving natural language explanations of why the system has created the subset in question.

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Human tracking using distributed vision systems

Cited by 51 publications

References 4 publications

A Survey of Computer Vision-Based Human Motion Capture

A Survey of Computer Vision-Based Human Motion Capture

A Fault-Tolerant Distributed Vision System Architecture for Object Tracking in a Smart Room

Research Steps Towards Human Sequence Evaluation

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