Omni-directional visual surveillance

Boult, Terrance E.; Gao, Xiang; Micheals, Ross J.; Eckmann, Michael

doi:10.1016/j.imavis.2003.09.005

Cited by 68 publications

(28 citation statements)

References 39 publications

(83 reference statements)

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“…3) Wide FOV Imaging: Catadioptric techniques combine lenses and mirrors in camera design and are often used to increase the camera FOV [53]- [60]. These techniques have significant impacts on a variety of real-world applications, including surveillance, autonomous navigation, virtual reality, and video conferencing [61]- [63].…”

Section: A Spatially Variant Filter 1) Depth Estimationmentioning

confidence: 99%

Computational Cameras: Convergence of Optics and Processing

Zhou

Nayar

2011

IEEE Trans. on Image Process.

105

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Abstract-A computational camera uses a combination of optics and processing to produce images that cannot be captured with traditional cameras. In the last decade, computational imaging has emerged as a vibrant field of research. A wide variety of computational cameras has been demonstrated to encode more useful visual information in the captured images, as compared with conventional cameras. In this paper, we survey computational cameras from two perspectives. First, we present a taxonomy of computational camera designs according to the coding approaches, including object side coding, pupil plane coding, sensor side coding, illumination coding, camera arrays and clusters, and unconventional imaging systems. Second, we use the abstract notion of light field representation as a general tool to describe computational camera designs, where each camera can be formulated as a projection of a high-dimensional light field to a 2-D image sensor. We show how individual optical devices transform light fields and use these transforms to illustrate how different computational camera designs (collections of optical devices) capture and encode useful visual information.

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Section: A Spatially Variant Filter 1) Depth Estimationmentioning

confidence: 99%

Computational Cameras: Convergence of Optics and Processing

Zhou

Nayar

2011

IEEE Trans. on Image Process.

105

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“…Omnidirectional cameras are promising candidates for monitoring multiple latent activities in the area of interest [4]. However, these kind of cameras have nonuniform resolution and are unable to provide close observations of particular targets.…”

Section: Introductionmentioning

confidence: 99%

Panoramic background modeling for PTZ cameras with competitive learning neural networks

Thurnhofer‐Hemsi

López‐Rubio

Domínguez

et al. 2017

2017 International Joint Conference on Neural Networks (IJCNN)

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Abstract-The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.

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“…Research on computer vision systems based on pan-tilt-zoom (PTZ) cameras has been intense for many years [2,4,8,13]. Nevertheless, there is a lack of a comprehensive theory which sets the foundations for the development of practical systems.…”

Section: Introductionmentioning

confidence: 99%

Motion Detection by Microcontroller for Panning Cameras

Benito-Picazo

López‐Rubio

Ortiz-de-Lazcano-Lobato

et al. 2017

Biomedical Applications Based on Natural and Artificial Computing

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Abstract. Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.

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Omni-directional visual surveillance

Cited by 68 publications

References 39 publications

Computational Cameras: Convergence of Optics and Processing

Computational Cameras: Convergence of Optics and Processing

Panoramic background modeling for PTZ cameras with competitive learning neural networks

Motion Detection by Microcontroller for Panning Cameras

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