Foreground segmentation with PTZ camera: a survey

Komagal, E.; Yogameena, B.

doi:10.1007/s11042-018-6104-4

Cited by 17 publications

(7 citation statements)

References 125 publications

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“…In the last years, it was published several previous surveys about abnormal human behavior recognition [6][7][8], human detection behavior [9,10], crowed behavior [11], datasets human recognition [12][13][14], and foreground segmentation [15]. Also, there are some research of fast violent detection [16][17][18][19][20], multi-features descriptors for human activity tracking and recognition [21], segmentation [22], and vision enhanced color fusion techniques [23].…”

Section: Video Approachmentioning

confidence: 99%

Detection Violent Behaviors: A Survey

Durães

Marcondes

Gonçalves

et al. 2021

Advances in Intelligent Systems and Computing

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Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided.

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Section: Video Approachmentioning

confidence: 99%

Detection Violent Behaviors: A Survey

Durães

Marcondes

Gonçalves

et al. 2021

Advances in Intelligent Systems and Computing

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“…Though the C-EFIC method is efficient and the results are good compared to other state-of-the-art methods [23], the affine approximation only holds for points close to the principal ray [24]. This means that points near the borders of the image will not be transformed accurately, especially when wide angle lenses are used.…”

Section: Related Workmentioning

confidence: 99%

Robust Pan/Tilt Compensation for Foreground–Background Segmentation

Allebosch

Hamme

Veelaert

et al. 2019

Sensors

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In this paper, we describe a robust method for compensating the panning and tilting motion of a camera, applied to foreground–background segmentation. First, the necessary internal camera parameters are determined through feature-point extraction and tracking. From these parameters, two motion models for points in the image plane are established. The first model assumes a fixed tilt angle, whereas the second model allows simultaneous pan and tilt. At runtime, these models are used to compensate for the motion of the camera in the background model. We will show that these methods provide a robust compensation mechanism and improve the foreground masks of an otherwise state-of-the-art unsupervised foreground–background segmentation method. The resulting algorithm is always able to obtain F 1 scores above 80 % on every daytime video in our test set when a minimal number of only eight feature matches are used to determine the background compensation, whereas the standard approaches need significantly more feature matches to produce similar results.

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“…One can also find sub-parts that concern moving cameras in object tracking and surveillance surveys [24,25,26,27]. However, the techniques addressing the case of moving cameras are more and more numerous and can be the target of whole study as proven by recent reviews [12,28,29]. In 2014, Elgammal [12] give an entire chapter on background subtraction techniques for moving camera classifying them into traditional and recent methods.…”

Section: Introductionmentioning

confidence: 99%

“…In 2014, Elgammal [12] give an entire chapter on background subtraction techniques for moving camera classifying them into traditional and recent methods. In 2018, Komagal and Yogameena [28] chose to review foreground segmentation approaches with a Pan Tilt Zoom (PTZ) camera but those techniques cannot usually be employed with freely moving cameras. In 2018, Yazdi and Bouwmans [29] presented the most complete survey on the subject, to the best of our knowledge.…”

Section: Introductionmentioning

confidence: 99%

Moving Objects Detection with a Moving Camera: A Comprehensive Review

Chapel¹,

Bouwmans²

2020

Preprint

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During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify and categorize the different existing methods found in the literature. For this purpose, we propose to classify these methods according to the choose of the scene representation: one plane or several parts. Inside these two categories, the methods are grouped according to eight different approaches: panoramic background subtraction, dual cameras, motion compensation, subspace segmentation, motion segmentation, plane+parallax, multi planes and split image in blocks. A reminder of methods for static cameras is provided as well as the challenges with both static and moving cameras. Publicly available datasets and evaluation metrics are also surveyed in this paper.

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Foreground segmentation with PTZ camera: a survey

Cited by 17 publications

References 125 publications

Detection Violent Behaviors: A Survey

Detection Violent Behaviors: A Survey

Robust Pan/Tilt Compensation for Foreground–Background Segmentation

Moving Objects Detection with a Moving Camera: A Comprehensive Review

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