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Incremental, scalable tracking of objects inter camera
- Computer Vision and Image Understanding
"... This paper presents a scaleable solution to the problem of tracking objects across spatially separated, uncalibrated cameras with non overlapping fields of view. The approach relies on the three cues of colour, relative size and movement between cameras to describe the relationship of objects betwee ..."
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Cited by 16 (3 self)
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This paper presents a scaleable solution to the problem of tracking objects across spatially separated, uncalibrated cameras with non overlapping fields of view. The approach relies on the three cues of colour, relative size and movement between cameras to describe the relationship of objects between cameras. This relationship weights the observation likelihood for correlating or tracking objects between cam-eras. Any individual cue alone has poor performance, but when fused together, a large boost in accuracy is gained. Unlike previous work, this paper uses an incre-mental technique to learning. The three cues are learnt in parallel and then fused together to track objects across the spatially separated cameras. The colour ap-pearance cue is incrementally calibrated through transformation matrices, while probabilistic links, modelling an object’s bounding box, between cameras represent the objects relative size. Probabilistic region links between entry and exit areas on cameras provide the cue of movement. The approach needs no pre colour or environment calibration and does not use batch processing. It works completely unsupervised, and is able to become more accurate over time as new evidence is accumulated. 1
Incremental Modelling of the Posterior Distribution of Objects for Inter and Intra Camera Tracking
"... This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method to create the spatio-temporal links between cameras, and thus model the posterior p ..."
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Cited by 5 (2 self)
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This paper presents a scalable solution to the problem of tracking objects across spatially separated, uncalibrated, non-overlapping cameras. Unlike other approaches this technique uses an incremental learning method to create the spatio-temporal links between cameras, and thus model the posterior probability distribution of these links. This can then be used with an appearance model of the object to track across cameras. It requires no calibration or batch preprocessing and becomes more accurate over time as evidence is accumulated. 1
Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People
"... Abstract—This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track ..."
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Cited by 5 (0 self)
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Abstract—This paper presents a novel approach for representing the spatio-temporal topology of the camera network with overlapping and non-overlapping fields of view (FOVs). The topology is determined by tracking moving objects and establishing object correspondence across multiple cameras. To track people successfully in multiple camera views, we used the Merge-Split (MS) approach for object occlusion in a single camera and the grid-based approach for extracting the accurate object feature. In addition, we considered the appearance of people and the transition time between entry and exit zones for tracking objects across blind regions of multiple cameras with non-overlapping FOVs. The main contribution of this paper is to estimate transition times between various entry and exit zones, and to graphically represent the camera topology as an undirected weighted graph using the transition probabilities. Keywords—Surveillance, multiple camera, people tracking, topology. I.
Auto-organized visual perception using distributed camera network
, 2009
"... Abstract. Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amoun ..."
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Cited by 3 (0 self)
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Abstract. Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. Cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. We propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. The images are decomposed sequentially into layers where redundant information are discarded. Without need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. The information sent by the network is then represented by a reduced and compact amount of data using a codification process. This framework allows structures to be retrieved and also the topology of the network. It can also provide the localization and trajectories of mobile objects. Experiments will present practical results in the case of a network containing 20 cameras observing a common scene. 1
calibration and patterns
"... Tracking objects across cameras by incrementally learning inter-camera colour ..."
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Tracking objects across cameras by incrementally learning inter-camera colour
Author manuscript, published in "The 8th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras- OMNIVIS, Marseille: France (2008)" Auto-Organized Visual Perception Using Distributed Camera Network
, 2008
"... Abstract. Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amoun ..."
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Abstract. Camera networks are complex vision systems difficult to control if the number of sensors is getting higher. With classic approaches, each camera has to be calibrated and synchronized individually. These tasks are often troublesome because of spatial constraints, and mostly due to the amount of information that need to be processed. Cameras generally observe overlapping areas, leading to redundant information that are then acquired, transmitted, stored and then processed. We propose in this paper a method to segment, cluster and codify images acquired by cameras of a network. The images are decomposed sequentially into layers where redundant information are discarded. Without need of any calibration operation, each sensor contributes to build a global representation of the entire network environment. The information sent by the network is then represented by a reduced and compact amount of data using a codification process. This framework allows structures to be retrieved and also the topology of the network. It can also provide the localization and trajectories of mobile objects. Experiments will present practical results in the case of a network containing 20 cameras observing a common scene. 1
Organized by
, 2007
"... Networked control systems are control systems comprised of the system to be controlled and of actuators, sensors and controllers, the operation of which is coordinated via a communication network. These systems are typically spatially distributed, may operate in an asynchronous manner, but have thei ..."
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Networked control systems are control systems comprised of the system to be controlled and of actuators, sensors and controllers, the operation of which is coordinated via a communication network. These systems are typically spatially distributed, may operate in an asynchronous manner, but have their operation coordinated to achieve desired overall objectives. Control systems with spatially distributed components have existed for several decades. Examples include control systems in chemical process plants, refineries, power plants, and airplanes. In the past, in such systems the components were connected via hardwired connections and the systems were designed to bring all the information from the sensors to a central location where the conditions were being monitored and decisions were taken on how to control the system. The control policies then were implemented via the actuators, which could be valves, motors etc. What is different today is that technology can put low cost processing power at remote locations via microprocessors and that information can be transmitted reliably via shared digital networks or even wireless connections. These technology driven
