Optimal sensor placement for surveillance of large spaces

Indu, S.; Chaudhury, Santanu; Mittal, Nitesh; Bhattacharyya, Asok

doi:10.1109/icdsc.2009.5289398

Cited by 41 publications

(25 citation statements)

References 4 publications

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“…Many networks are controlled by a human operator to avoid the data processing requirements for automated reconfiguration control. One option explored to circumvent an on-line control algorithm was to analyze the workspace a priori, and then optimize for camera placement to maximize possible visibility [125]- [127]. Alternatively, cameras may be controlled on-line to reconfigure for objectives such as target resolution, workspace coverage and visibility, and target tracking, [124], [128].…”

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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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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“…For example, [22] and [23] consider energy aware allocation of vision tasks to cameras in order to increase the lifetime of battery-operated networks, while in [24] the authors propose an algorithm to optimize resource allocation in camera networks. Many works deal with the problem of configuring a camera network in such a way as to maximize the coverage of a monitored area with static cameras such as [25], [26], and usually determine the number, placement position, and orientation of cameras in the area. Our work builds on such works assuming that a deployment has already taken place and the positions of the cameras are known and fixed.…”

Section: Related Workmentioning

confidence: 99%

Optimizing the Detection Performance of Smart Camera Networks Through a Probabilistic Image-Based Model

Kyrkou

Christoforou

Timotheou

et al. 2018

IEEE Trans. Circuits Syst. Video Technol.

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Networks of smart cameras, equipped with on-board processing and communication infrastructure, are increasingly being deployed in a variety of different application fields, such as security and surveillance, traffic monitoring, industrial monitoring, and critical infrastructure protection. The task(s) that a network of smart cameras executes in these applications, e.g., activity monitoring, object identification, can be severely degraded because of errors in the detection module. However, in most cases higher-level tasks and decision making processes in smart camera networks (SCNs) assume ideal detection capabilities for the cameras, which is often not the case due to the probabilistic nature of the detection process, especially for low-cost cameras with limited capabilities. Realizing that it is necessary to introduce robustness in the decision process this paper presents results towards uncertainty-aware SCNs. Specifically, we introduce a flexible uncertainty model that can be used to characterize the detection behaviour in a camera network. We also show how to utilize the model to formulate detectionaware optimization algorithms that can be used to reconfigure the network in order to improve the overall detection efficiency and thus increase the effective number of detected targets. We evaluate our proposed model and algorithms using a network of Raspberry-Pi-based smart cameras that reconfigure in order to improve the detection performance based on the position of targets in the area. Experimental results in the lab as well as in a human monitoring application and extensive simulation results, indicate that the proposed solutions are able to improve the robustness and reliability of SCNs.

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“…This has been done using the formulation discussed in [14]. The sensing space is logically divided into cubical blocks, voxels.…”

Section: Problem Definition and Space Modellingmentioning

confidence: 99%

“…For avoiding occlusion we prefer to cover each voxel by atleast two cameras. Based on these constraints the objective function for camera placement referred as the Camera Coverage Metric given in equation 1 has been adopted from [14].…”

Section: A Camera Placementmentioning

confidence: 99%

Camera and Light Source Placement: A Multi-Objective Approach

Garg

Indu

Chaudhury

2011

2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics

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Optimal placement of visual sensors along with good lighting conditions is indispensable for the successful execution of surveillance applications. Limited field-of-view, depth-of-field, occlusion due to presence of different objects in the scene form the major constraints for visual sensor placement. While over/under exposed objects, shadowing and light rays directly incident on the camera lens are some of the constraints for light source placement. Because of the nature of the constraints and complexity of the problem, the placement problem is considered to be a multi-objective global optimization problem. The paper outlines the camera and light source location optimization problem with multiple objective functions. Multi-Objective Genetic Algorithm is used to maximize the camera coverage with optimum illumination of the sensing space.

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Optimal sensor placement for surveillance of large spaces

Cited by 41 publications

References 4 publications

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

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

Optimizing the Detection Performance of Smart Camera Networks Through a Probabilistic Image-Based Model

Camera and Light Source Placement: A Multi-Objective Approach

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