Robust detection of moving objects in video sequences through rough set theory framework

Chiranjeevi, P.; Sengupta, Somnath

doi:10.1016/j.imavis.2012.06.015

Cited by 28 publications

(27 citation statements)

References 11 publications

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“…But while implementing practically, some issues or critical conditions arises and may lead to disturbance of the whole procedure. Various major challenging issues are listed below in case of visual surveillances system (Chiranjeevi and Sengupta, 2012 https://github.com/andrewssobral/ bgslibrary/wiki/Release-notes; https://github.com/andrewssobral/bgslibrary/wiki/List-ofavailable-algorithms; http://www.dis.uniroma1.it/~bloisi/software/imbs.html; I2R Dataset, http://perception.i2r.a-star.edu.sg/bk model/bkindex.html; CSIR-CSIO Moving Object Thermal Infrared Imagery Dataset (MOTI-ID): CSIR Dataset 09, http://www.vcipl.okstate.edu/otcbvs/bench/).…”

Section: Challenges and Issuesmentioning

confidence: 99%

Performance analysis of moving object detection using BGS techniques in visual surveillance

Sharma¹,

Lohan²

2019

IJSTDS

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Over the last decennium, the object detection is the pivotal step in any machine vision and image processing application. It is the initial step applied to extract most informative pixel from the video stream. Many algorithms are available in literature for extraction of visual information or foreground object from video sequence. This paper also provides a detailed overview of both conventional and traditional approaches used for detection of object. This paper explores various related methods, major challenges, applications, resources such as datasets, web-sources, etc. This paper presents a detailed overview of a moving object detection using background subtraction techniques in the video surveillance system that provide safety in cities, towns or home when video sequence is captured using IP cameras. The experimental work of this paper is performed over change detection, I2R, and wallflower datasets. The experimental work also depicts a comparative analysis of some of the peer methods. This work demonstrates several performance metrics to check robustness of the compared state-of-the-art methods.

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Section: Challenges and Issuesmentioning

confidence: 99%

Performance analysis of moving object detection using BGS techniques in visual surveillance

Sharma¹,

Lohan²

2019

IJSTDS

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“…Moreover, the equation renovated corresponding to the transformation motive for the background with fractional derivative. The equation is derived from (1) as follows, (2) As given earlier, the Fractional Calculus is a component of the exponential analysis that frames to real or complex integers the differential order based integral functions. Because of the simplification feature of the fractional derivative and also the integral operator to a non-integer N th order based on variant models.…”

Section: A Static Background Separation Using Fractional Derivativementioning

confidence: 99%

“…In that process, some issues may happen in moving object detection with high accuracy rate, but which is more important in face identification and tracking. A standard explanation aids to variant the regions of concern in the surveillance videos for particular instances, based on the background region of moving objects [2]. The basic model for face recognition from surveillance video is presented in Fig.1.…”

Section: Introductionmentioning

confidence: 99%

Towards Enhanced Anomaly Object Detection and Face Recognition (EAODFR) in Surveillance Videos using Recurrent Neural Networks

Ragupathy*,

Vivekanandan

2019

IJRTE

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In the present decade, anomaly object detection and face recognition from surveillance videos from diverse environments have become interesting and challenging research areas in computer vision. This paper works on developing an Enhanced Anomaly Object Detection and Face Recognition (EAODFR) model using Recurrent Neural Networks (RNN). Moreover, fractional derivative based background separation has been incorporated for framing efficient background subtraction model and foreground segmentation with appropriate pixel definitions on each frame of the surveillance videos. The Region of Interest detection has been done using optimal thresholding and for detecting anomaly objects. Further, efficient face recognition has been accomplished by designing the Recurrent Neural Networks (RNN), which is implemented with Long Short-Term Memory (LSTM). The recurrent NN are trained in terms of determining anomalous objects using the extracted features in the each frame of the video. The obtained results are analyzed in terms of precision, recall and f-measure and compared with some existing face recognition models. The comparative analysis provides better results and outperforms others.

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“…A further improvement to the histon is proposed by Mushrif and Ray, 18 where a new histogram-like representation, named roughness index, is introduced. Recently, the roughness-based method has been used in different applications like image retrieval, 19 detection of moving objects, 20 color text segmentation, 21 and medical imaging. 22 However, this rough set-based methodology has not been explored for different features, rather than the RGB color representation.…”

Section: Introductionmentioning

confidence: 99%

Integration of color and texture cues in a rough set–based segmentation method

Lizarraga-Morales

Sánchez-Yáñez

Ayala-Ramírez

et al. 2014

J. Electron. Imaging

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Abstract. We propose the integration of color and texture cues as an improvement of a rough set-based segmentation approach, previously implemented using only color features. Whereas other methods ignore the information of neighboring pixels, the rough set-based approximations associate pixels locally. Additionally, our method takes into account pixel similarity in both color and texture features. Moreover, our approach does not require cluster initialization because the number of segments is determined automatically. The color cues correspond to the a and b channels of the CIELab color space. The texture features are computed using a standard deviation map. Experiments show that the synergistic integration of features in this framework results in better segmentation outcomes, in comparison with those obtained by other related and state-of-the-art methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Robust detection of moving objects in video sequences through rough set theory framework

Cited by 28 publications

References 11 publications

Performance analysis of moving object detection using BGS techniques in visual surveillance

Performance analysis of moving object detection using BGS techniques in visual surveillance

Towards Enhanced Anomaly Object Detection and Face Recognition (EAODFR) in Surveillance Videos using Recurrent Neural Networks

Integration of color and texture cues in a rough set–based segmentation method

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