Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems

Xue, Kang; Liu, Yue; Ogunmakin, Gbolabo; Chen, Jing; Zhang, Jiangen

doi:10.1007/s00138-012-0426-4

Cited by 35 publications

(18 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The key problem to perform background subtraction is to register the camera image with the panoramic background model at different scale. Xue et al (Xue et al, 2013) proposed a method that relies on a panoramic back-ground model and a hierarchy of images of the scene at different scales. A match is found between the current image and images in the hierarchy, then the match is propagated to upper level until registration with the panoramic background.…”

Section: State Of the Artmentioning

confidence: 99%

Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera

Chapel

Guillou

Bouakaz

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

The detection of moving objects in the video stream of a moving camera is a complex task. Static objects appear moving in the video stream as moving objects. Thus, it is difficult to identify motions that belong to moving objects because they are hidden by those of static objects. To detect moving objects we propose a novel geometric constraint based on 2D and 3D information. A sparse reconstruction of the visible part of the scene is performed in order to detect motions in the 3D space where the scene perception is not deformed by the camera motion. A first labeling estimation is performed in the 3D space and then apparent motions in the video stream of the moving camera are used to validate the estimation. Labels are computed from confidence values which are updated at each frame according to the geometric constraint. Our method can detect several moving objects in complex scenes with high parallax.

show abstract

Section: State Of the Artmentioning

confidence: 99%

Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera

Chapel

Guillou

Bouakaz

2017

Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

View full text Add to dashboard Cite

show abstract

“…However, traditional background subtraction algorithms assume the cameras are static and this leads to false detection when the camera moves [6], [7]. Due to this camera movement, even pixels belonging to static objects appear to move in the camera frame (called ego-motion effect).…”

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)

View full text Add to dashboard Cite

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.

show abstract

“…Compared to other approaches, such as optical flow, this approach is computationally affordable for real‐time applications, is independent of moving object velocity, and is not subject to the foreground aperture problem. However, traditional background subtraction algorithms assume the cameras are static, and this leads to false detection when the camera moves (Kim, Yun, Yi, Kim, & Choi, ; Xue, Liu, Ogunmakin, Chen, & Zhang, ). Due to this camera movement, even pixels belonging to static objects appear to move in the camera frame (called ego‐motion effect).…”

Section: Introductionmentioning

confidence: 99%

Panorama construction for PTZ camera surveillance with the neural gas network

Thurnhofer‐Hemsi¹,

López‐Rubio²,

Domínguez³

et al. 2017

Expert Systems

View full text Add to dashboard Cite

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 the neural gas network and a subsequent piecewise linear interpolation by Delaunay triangulation. Furthermore, an ensemble model of neural gas networks is also proposed. The approach can handle arbitrary camera directions and zooms for a pan‐tilt‐zoom camera‐based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed methods are effective and suitable to use for real‐time video surveillance applications.

show abstract

Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems

Cited by 35 publications

References 16 publications

Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera

Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera

Panoramic background modeling for PTZ cameras with competitive learning neural networks

Panorama construction for PTZ camera surveillance with the neural gas network

Contact Info

Product

Resources

About