CVABS: moving object segmentation with common vector approach for videos

Işık, Şahin; Özkan, Kemal; Gerek, Ömer Nezih

doi:10.1049/iet-cvi.2018.5642

Cited by 10 publications

(3 citation statements)

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“…Another method, SWCD [12], combines the dynamic controllers of SuBSENSE with a sliding window approach to update background frames. Finally, CVABS [13], a recent subspacebased method, utilizes dynamic self-adjustment mechanisms like SuBSENSE and PAWCS to update the background model.…”

Section: B Methods Based On Dynamic Feedback Mechanismmentioning

confidence: 99%

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Weakly Supervised Realtime Dynamic Background Subtraction

Bahri¹,

Ray²

2023

Preprint

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Background subtraction is a fundamental task in computer vision with numerous real-world applications, ranging from object tracking to video surveillance. However, dynamic backgrounds can pose a significant challenge in this problem. While various methods have been proposed for background subtraction, supervised deep learning-based techniques are currently considered state-of-the-art. However, these methods require pixelwise ground-truth labeling, which can be time-consuming and expensive. In this work, we propose a weakly supervised framework that can perform background subtraction without requiring perpixel ground-truth labels. Our framework is trained on a moving object-free sequence of images and comprises two networks. The first network is an autoencoder that generates static background images and prepares dynamic background images for training the second network. The dynamic background images are obtained by thresholding the background-subtracted images. The second network is a U-Net that uses the same moving object-free video for training and the dynamic background images as pixel-wise ground-truth labels. During the test phase, the input images are processed by the autoencoder and U-Net, which generate static and dynamic background images, respectively. The dynamic background image helps remove dynamic motion from the background subtracted image, enabling us to obtain a foreground image that is free of dynamic artifacts. To demonstrate the effectiveness of our method, we conducted experiments on selected categories of the CDnet 2014 dataset and the I2R dataset. Our method outperformed all top-ranked unsupervised methods. It also surpassed one of the two existing weakly supervised methods, while achieving comparable results to the other method but with a shorter running time. Our proposed method is online, realtime, efficient, and requires minimal frame-level annotation, making it suitable for a wide range of real-world applications.

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Section: B Methods Based On Dynamic Feedback Mechanismmentioning

confidence: 99%

“…Another class of methods involves dynamically adjusting their parameters through feedback mechanisms. The SuB-SENSE method [10] was the pioneering method in this category, and it has inspired several other methods, including PAWCS [11], SWCD [12], and CVABS [13].…”

Section: Introductionmentioning

confidence: 99%

Weakly Supervised Realtime Dynamic Background Subtraction

Bahri¹,

Ray²

2023

Preprint

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“…In this paper, the implementations and comparison of classification methods, K-Nearest Neighbor (K-NN) [6,20,29], Common Matrix Approach (CMA) [30,31], Support Vector Machine (SVM) and Convolutional Neural Network (CNN), are proposed for the classification of face images represented with the feature matrices extracted from the 2DPCA, 2DSVD and 2DFDA methods. CMA can be considered as a 2D version of the Common Vector Approach (CVA) which is widely used in speech [32][33][34] and image processing [35][36][37] and also in motor fault diagnosis [38]. CMA finds a unique common matrix including the common or invariant features of each face class.…”

Section: Introductionmentioning

confidence: 99%

Face Recognition by Using 2D Orthogonal Subspace Projections

Ergin¹,

Işık²,

Gülmezoğlu³

2021

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In this paper, the implementations and comparison of some classifiers along with 2D subspace projection approaches have been carried out for the face recognition problem. For this purpose, the well-known classifiers such as K-Nearest Neighbor (K-NN), Common Matrix Approach (CMA), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are conducted on low dimensional face representations that are determined from 2DPCA-, 2DSVD- and 2DFDA approaches. CMA, which is a 2D version of the Common Vector Approach (CVA), finds a common matrix for each face class. From the experimental results, we have observed that the SVM presents a dominant performance in general. When overall results of all datasets are considered, CMA is slightly superior to others in case of 2DPCA- and 2DSVD-based features matrices of the AR dataset. On the other side, CNN is better than other classifiers when it comes to develop a face recognition system based on original face samples and 2DPCA-based feature matrices of the Yale dataset. The experimental results indicate that use of these feature matrices with CMA, SVM, and CNN in classification problems is more advantageous than the use of original pixel matrices in the sense of both processing time and memory requirement.

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