Exploiting distinctive visual landmark maps in pan–tilt–zoom camera networks

Bimbo, Alberto Del; Dini, F.; Lisanti, Giuseppe; Pernici, Federico

doi:10.1016/j.cviu.2010.01.007

Cited by 37 publications

(27 citation statements)

References 41 publications

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“…The active ones are directed by simple geometric reasoning and static rules to acquire frontal face images. Recent work by Del Bimbo et al [17] addresses the difficulty in positioning active cameras according to seemingly simple rules. Sommerlade and Reid [18] extend the approach of [14] to the scene domain, facilitating camera hand-off.…”

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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“…Some alternative approaches have also been presented in [2,24,25]. In [24] multiple consecutive frames were used to approximate target depth.…”

Section: Related Workmentioning

confidence: 99%

“…You et al [25] estimated the relationship between the static and the active camera using a homography for each image of the mosaic derived from the slave camera. Del Bimbo et al [2] relied on feature point matching to automatically estimate a homography ( ), relating the master and slave views with respect to the reference plane. is used to perform an online mapping between the feet locations in the master to the slave camera and also determine the reference plane vanishing line from the one manually marked on the static view.…”

Section: Related Workmentioning

confidence: 99%

“…While several advantages can be outlined, intercamera calibration is the major bottleneck of this configuration, since an accurate mapping from image coordinates to pan-tilt space requires depth information and distortion correction, as illustrated in Figure 1. The existing approaches [1][2][3][4] rely on rough approximations or on the use of multiple static devices to perform triangulation. Also, they assume the pinhole model for the static camera.…”

Section: Introductionmentioning

confidence: 99%

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A Master-Slave Calibration Algorithm with Fish-Eye Correction

Neves

Moreno²,

Proença³

2015

Mathematical Problems in Engineering

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Surveillance systems capable of autonomously monitoring vast areas are an emerging trend, particularly when wide-angle cameras are combined with pan-tilt-zoom (PTZ) cameras in a master-slave configuration. The use of fish-eye lenses allows the master camera to maximize the coverage area while the PTZ acts as a foveal sensor, providing high-resolution images of regions of interest. Despite the advantages of this architecture, the mapping between image coordinates and pan-tilt values is the major bottleneck in such systems, since it depends on depth information and fish-eye effect correction. In this paper, we address these problems by exploiting geometric cues to perform height estimation. This information is used both for inferring 3D information from a single static camera deployed on an arbitrary position and for determining lens parameters to remove fish-eye distortion. When compared with the previous approaches, our method has the following advantages: (1) fish-eye distortion is corrected without relying on calibration patterns; (2) 3D information is inferred from a single static camera disposed on an arbitrary location of the scene.

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“…The method applied a game-theory approach to reconfigure the cameras for single and multitarget tracking. Target tracking and cross camera association was the goal of the PTZ reconfiguration method proposed in [140]. The objective was to map a target's feet to their face in two different views to improve tracking across the network and simultaneously the visibility.…”

Section: Multi-camera Reconfiguration For 2d Tasksmentioning

confidence: 99%

Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects

Nuger

Benhabib

2018

J Intell Robot Syst

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This thesis proposes a multi-camera active-vision reconfiguration system which selects camera poses online to improve the shape recovery of a priori unknown, markerless, deforming objects in dynamic environments. The objectives of shape recovery are defined as surface sampling accuracy, and shape completeness. The completeness objective is generalized for both solid and surface-based objects as the maximization of surface visibility. Thus, improving the recovered shape of target objects is shown to be analogous to maximizing the surface area visibility. This improvement is achieved through the on-line reconfiguration of multiple cameras. The system developed herein is composed of a shape recovery method, a robust tracking algorithm, and a multi-camera reconfiguration method.The shape recovery method is based on a modular fusion technique that produces a complete 3D mesh-model of the target object. The method fuses triangulation data with a visual hull to maximize recovery accuracy. The modularity of the method lies in the ability to modify data filtering techniques to improve modelling accuracy, and interchange stereo correspondence features. The adaptive particle filtering algorithm produces a deformation estimation of the recovered model from the tracking data. The algorithm automatically adapts to the quantity of tracking data available and changes in the object's dynamics. The modularity of the algorithm allows modifications in terms of the number of particles and motion models for task-specific implementations. The reconfiguration method consists of a robust stereovisibility objective function, workspace discretization, and a path planner. The complete system can iii improve the shape recovery of a priori unknown deforming objects in obstacle-laden environments when compared to static camera methods.To validate the proposed system, extensive simulations and experiments were conducted. The simulations tested the system in comparison to static multi-camera systems, and ideal camera placement where the object model was a priori known and the camera's dynamics were unconstrained. Simulation results showed the proposed methodology outperformed static cameras and approached the performance of ideal camera placement in a dynamic, obstacle-laden environment. The experimental results showed similar improvement in shape recovery when comparison to a static camera system in an obstacle-laden environment.iv

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Exploiting distinctive visual landmark maps in pan–tilt–zoom camera networks

Cited by 37 publications

References 41 publications

Cognitive visual tracking and camera control

Cognitive visual tracking and camera control

A Master-Slave Calibration Algorithm with Fish-Eye Correction

Multi-Camera Active-Vision for Markerless Shape Recovery of Unknown Deforming Objects

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