Background Subtraction Using Dominant Directional Pattern

Roy, Kaushik; Arefin, Rifat; Makhmudkhujaev, Farkhod; Chae, Oksam; Kim, Jaemyun

doi:10.1109/access.2018.2846749

Cited by 8 publications

(7 citation statements)

References 33 publications

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“…Background subtraction algorithm includes four major steps: preprocessing, background modeling, foreground detection and data validation [25]. Background modeling is the core of background subtraction algorithm, and many different methods have been proposed over the recent years [26], [27], [28], [29], [30], [31].…”

Section: ) Mean Background Subtractionmentioning

confidence: 99%

Automatic Visual Pit Detection System for Bottom Surface of Cylindrical Lithium Battery

Guo

Xing

2023

IEEE Access

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The pit on the bottom metal surface is one of the important indicators of cylindrical lithium battery surface defect detection. There are many complex factors in the detection of pit: non-uniform illumination of images, uneven reflection of the metal surface, low surface finishing, stains, rust and scratches. To solve these problems, a method for pit detection based on machine vision is proposed. Firstly, the grayscale distribution curve is extracted along the vertical direction of the bottom metal surface. Secondly, the grayscale difference model which is not sensitive to illumination distribution and noises is used to extract gray discontinuous points in a grayscale distribution curve. According to the reflective feature of the metal surface, the adaptive threshold of discontinuous points extraction is determined based on mean background subtraction. Finally, three feature parameters including gray value features and region features are used as the input of support vector machine (SVM) classifier to train and extract the pit region. The algorithm is evaluated on the self-built image database. The experimental results indicate the non-uniform illumination and uneven reflection have no effect on pit detection. Compared with the related well-established methods, our proposed algorithm can provide a better detection effect-the Recall, Precision and FNR are 0.982, 0.991 and 0.018 respectively.INDEX TERMS Machine vision, grayscale difference model, cylindrical lithium battery, defect detection, mean background subtraction.

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Section: ) Mean Background Subtractionmentioning

confidence: 99%

Automatic Visual Pit Detection System for Bottom Surface of Cylindrical Lithium Battery

Guo

Xing

2023

IEEE Access

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“…The camera-based system presents several challenges. First and foremost, extracting foreground features consists of background modeling in redgreen-blue (RBG) image space that is a challenging task in the context of real-world conditions considering the issues of shadows and light intensity [24]. Second, fall activity in no/low light can only be detected if an infrared (IR) light source is deployed along the cameras.…”

Section: Related Workmentioning

confidence: 99%

Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

Shah

Tahir

Kernec

et al. 2022

Neural Comput & Applic

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The health status of an older or vulnerable person can be determined by looking into the additive effects of aging as well as any associated diseases. This status can lead the person to a situation of ‘unstable incapacity’ for normal aging and is determined by the decrease in response to the environment and to specific pathologies with apparent decrease of independence in activities of daily living (ADL). In this paper, we use micro-Doppler images obtained using a frequency-modulated continuous wave radar (FMCW) operating at 5.8 GHz with 400 MHz bandwidth as the sensor to perform assessment of this health status. The core idea is to develop a generalized system where the data obtained for ADL can be portable across different environments and groups of subjects, and critical events such as falls in mature individuals can be detected. In this context, we have conducted comprehensive experimental campaigns at nine different locations including four laboratory environments and five elderly care homes. A total of 99 subjects participated in the experiments where 1453 micro-Doppler signatures were recorded for six activities. Different machine learning, deep learning algorithms and transfer learning technique were used to classify the ADL. The support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) provided adequate classification accuracies for particular scenarios; however, the autoencoder neural network outperformed the mentioned classifiers by providing classification accuracy of ~ 88%. The proposed system for fall detection in elderly people can be deployed in care centers and is application for any indoor settings with various age group of people. For future work, we would focus on monitoring multiple older adults, concurrently in indoor settings using continuous radar sensor data stream which is limitation of the present system.

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“…Our previous work [31] introduced an adaptive local texture feature (ALTF) to deal with sudden and gradual illumination changes using Weber's law and a sample consensus scheme. To address the illumination variation issue, a new spatial feature descriptor, which extracts the prominent directional information in the local neighborhood of a pixel, was introduced in [32]. The LBP and its improved descriptors are computational simplicity and can effectively manage illumination changes.…”

Section: Compared With Histogram Of Oriented Gradients (Hog) Andmentioning

confidence: 99%

Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

et al. 2020

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Background subtraction is commonly employed in foreground object detection in urban traffic scenes. Most of the current color or texture feature-based background subtraction models are easily contaminated by sudden and gradual illumination variations in urban traffic scenes. To resolve this deficiency, an adaptive local median texture feature, which extracts the adaptive distance threshold employing the median information in a predefined local region of a pixel and Weber's law, is introduced. In addition, a sample consensus-based model that evolved from portable visual background extractor is proposed using an adaptive local median texture feature. Then, the foreground is labeled by comparing the input video frames feature with the model. Moreover, to adapt the dynamic background, the random update scheme is used to update the model. Extensive experimental results on the public Change Detection data set of 2014 (CDnet2014) and the real-world urban traffic videos demonstrate that our background subtraction method is superior to the other state-of-the-art texture-feature-based methods. The qualitative and quantitative results show the encouraging efficiency of the proposed technique to deal with sudden and gradual illumination variations in real-world urban traffic scenes. INDEX TERMS background modeling, illumination variations, local median texture feature, urban traffic scenes

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Background Subtraction Using Dominant Directional Pattern

Cited by 8 publications

References 33 publications

Automatic Visual Pit Detection System for Bottom Surface of Cylindrical Lithium Battery

Automatic Visual Pit Detection System for Bottom Surface of Cylindrical Lithium Battery

Data portability for activities of daily living and fall detection in different environments using radar micro-doppler

Background Subtraction Using an Adaptive Local Median Texture Feature in Illumination Changes Urban Traffic Scenes

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