Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors

Alonso, Mercedes; Brunete, Alberto; Hernando, Miguel; Gambao, Ernesto

doi:10.1109/access.2019.2948321

Cited by 17 publications

(7 citation statements)

References 42 publications

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“…Due to the huge number of bands in Hyper Spectral Images (HSI), dimension reduction is required prior to processing. After dimensionality reduction with HSI, a hyperspectral visual attention model (HVAM) [38] is used to detect anomalies. Remove the noise with a curvature filter and then use the background subtraction method to acquire the first result.…”

Section: Object Detection Using Background Subtraction Methodsmentioning

confidence: 99%

Realtime Object Detection Through M-ResNet in Video Surveillance System

Prabu¹,

Gnanasekar²

2023

Intelligent Automation &Amp; Soft Computing

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Object detection plays a vital role in the video surveillance systems. To enhance security, surveillance cameras are now installed in public areas such as traffic signals, roadways, retail malls, train stations, and banks. However, monitoring the video continually at a quicker pace is a challenging job. As a consequence, security cameras are useless and need human monitoring. The primary difficulty with video surveillance is identifying abnormalities such as thefts, accidents, crimes, or other unlawful actions. The anomalous action does not occur at a higher rate than usual occurrences. To detect the object in a video, first we analyze the images pixel by pixel. In digital image processing, segmentation is the process of segregating the individual image parts into pixels. The performance of segmentation is affected by irregular illumination and/or low illumination. These factors highly affect the real-time object detection process in the video surveillance system. In this paper, a modified ResNet model (M-Resnet) is proposed to enhance the image which is affected by insufficient light. Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream. The proposed model shows better results in the metrics like precision, recall, pixel accuracy, etc., and finds a reasonable improvement in the object detection.

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Section: Object Detection Using Background Subtraction Methodsmentioning

confidence: 99%

Realtime Object Detection Through M-ResNet in Video Surveillance System

Prabu¹,

Gnanasekar²

2023

Intelligent Automation &Amp; Soft Computing

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“…HAR based on cameras has been popular in the past. Some researchers separated the image background from the human and then used machine learning or deep learning to extract features [5,6]. Espinosa et al [7] separated the person in the picture from the background and extracted the ratio of length to width of the human body to recognize standing and falling.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Zhao

Zhou

et al. 2022

Applied Sciences

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Human activity recognition (HAR) can effectively improve the safety of the elderly at home. However, non-contact millimeter-wave radar data on the activities of the elderly is often challenging to collect, making it difficult to effectively improve the accuracy of neural networks for HAR. We addressed this problem by proposing a method that combines the improved principal component analysis (PCA) and the improved VGG16 model (a pre-trained 16-layer neural network model) to enhance the accuracy of HAR under small-scale datasets. This method used the improved PCA to enhance features of the extracted components and reduce the dimensionality of the data. The VGG16 model was improved by deleting the complex Fully-Connected layers and adding a Dropout layer between them to prevent the loss of useful information. The experimental results show that the accuracy of our proposed method on HAR is 96.34%, which is 4.27% higher after improvement, and the training time of each round is 10.88 s, which is 12.8% shorter than before.

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“…The optimization of background estimation algorithms with the use of genetic algorithms was considered in the works of [37,38]. The task of optimizing serial connection of algorithms in a context of assembly line balancing using PSO (Particle Swarm Optimization) is considered in the paper in [39].…”

Section: Related Workmentioning

confidence: 99%

Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

Matczak

Mazurek

2021

Remote Sensing

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Background estimation algorithms are important in UAV (Unmanned Aerial Vehicle) vision tracking systems. Incorrect selection of an algorithm and its parameters leads to false detections that must be filtered by the tracking algorithm of objects, even if there is only one UAV within the visibility range. This paper shows that, with the use of genetic optimization, it is possible to select an algorithm and its parameters automatically. Background estimation algorithms (CNT (CouNT), GMG (Godbehere-Matsukawa-Goldberg), GSOC (Google Summer of Code 2017), MOG (Mixture of Gaussian), KNN (K–Nearest Neighbor–based Background/Foreground Segmentation Algorithm), MOG2 (Mixture of Gaussian version 2), and MEDIAN) and the reference algorithm of thresholding were tested. Monte Carlo studies were carried out showing the advantages of the MOG2 algorithm for UAV detection. An empirical sensitivity analysis was presented that rejected the MEDIAN algorithm.

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Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors

Cited by 17 publications

References 42 publications

Realtime Object Detection Through M-ResNet in Video Surveillance System

Realtime Object Detection Through M-ResNet in Video Surveillance System

Human Activity Recognition Based on Non-Contact Radar Data and Improved PCA Method

Comparative Monte Carlo Analysis of Background Estimation Algorithms for Unmanned Aerial Vehicle Detection

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