A review on the artificial neural network approach to analysis and prediction of seismic damage in infrastructure

Shokri, Manouchehr; Tavakoli, Kian

doi:10.1504/ijhm.2019.104386

Cited by 48 publications

(14 citation statements)

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“…1 slap/punch, 2 kicking, 3 pushing, 4 pat on back, 5 point finger, 6 hugging, 7 giving object, 8 touch pocket, 9 shaking hands, 10 walking towards, 11 walking apart.…”

Section: Recognition Accuracymentioning

confidence: 99%

“…However, in the proposed system, we focused on two-person interactions, i.e., human-human interaction. Extensive research has been carried out in the field of vision-based HAR systems but there remains a need for an adaptive and sustainable Sustainability 2021, 13, 970 2 of 30 HAR system that is effective regardless of the environment [5][6][7][8][9][10][11]. The main aim of this research work is to develop a novel, reliable and sustainable vision-based HAR system based on our unique set of features.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

et al. 2021

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Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems.

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“…1 slap/punch, 2 kicking, 3 pushing, 4 pat on back, 5 point finger, 6 hugging, 7 giving object, 8 touch pocket, 9 shaking hands, 10 walking towards, 11 walking apart.…”

Section: Recognition Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

et al. 2021

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“…With the MMLESAC plane fitting technique, we improved depth segmentation over existing MLESAC and RANSAC methods. MLESAC [31][32][33][34] follows RANSAC's [35][36][37][38][39][40] basic idea which produces hypothetical results based on consecutive marginal correspondence sets; in contrast, the other remaining correspondences are used to check the quality of the hypothesis. Although, based on the probabilistic approach, MLESAC evaluates via the random sampling hypothesis, it does not presume any such complexity in the earlier matching stage which is used to provide its data.…”

Section: Multi-objects Segmentation Using Mmlesacmentioning

confidence: 99%

Automated Sustainable Multi-Object Segmentation and Recognition via Modified Sampling Consensus and Kernel Sliding Perceptron

2020

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Object recognition in depth images is challenging and persistent task in machine vision, robotics, and automation of sustainability. Object recognition tasks are a challenging part of various multimedia technologies for video surveillance, human–computer interaction, robotic navigation, drone targeting, tourist guidance, and medical diagnostics. However, the symmetry that exists in real-world objects plays a significant role in perception and recognition of objects in both humans and machines. With advances in depth sensor technology, numerous researchers have recently proposed RGB-D object recognition techniques. In this paper, we introduce a sustainable object recognition framework that is consistent despite any change in the environment, and can recognize and analyze RGB-D objects in complex indoor scenarios. Firstly, after acquiring a depth image, the point cloud and the depth maps are extracted to obtain the planes. Then, the plane fitting model and the proposed modified maximum likelihood estimation sampling consensus (MMLESAC) are applied as a segmentation process. Then, depth kernel descriptors (DKDES) over segmented objects are computed for single and multiple object scenarios separately. These DKDES are subsequently carried forward to isometric mapping (IsoMap) for feature space reduction. Finally, the reduced feature vector is forwarded to a kernel sliding perceptron (KSP) for the recognition of objects. Three datasets are used to evaluate four different experiments by employing a cross-validation scheme to validate the proposed model. The experimental results over RGB-D object, RGB-D scene, and NYUDv1 datasets demonstrate overall accuracies of 92.2%, 88.5%, and 90.5% respectively. These results outperform existing state-of-the-art methods and verify the suitability of the method.

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“…However, wearable sensors possess certain challenges in recognizing human activities due to a lack of reliable contextual information caused by inconsistency in human body movement, lapses during activity recording and other interruptions like resting etc. [3]. Therefore, an efficient model is required that can correctly detect complex human postures and their significance.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

2020

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Human behavior modeling (HBM) is a challenging classification task for researchers seeking to develop sustainable systems that precisely monitor and record human life-logs. In recent years, several models have been proposed; however, HBM remains an inspiring problem that is only partly solved. This paper proposes a novel framework of human behavior modeling based on wearable inertial sensors; the system framework is composed of data acquisition, feature extraction, optimization and classification stages. First, inertial data is filtered via three different filters, i.e., Chebyshev, Elliptic and Bessel filters. Next, six different features from time and frequency domains are extracted to determine the maximum optimal values. Then, the Probability Based Incremental Learning (PBIL) optimizer and the K-Ary tree hashing classifier are applied to model different human activities. The proposed model is evaluated on two benchmark datasets, namely DALIAC and PAMPA2, and one self-annotated dataset, namely, IM-LifeLog, respectively. For evaluation, we used a leave-one-out cross validation scheme. The experimental results show that our model outperformed existing state-of-the-art methods with accuracy rates of 94.23%, 94.07% and 96.40% over DALIAC, PAMPA2 and IM-LifeLog datasets, respectively. The proposed system can be used in healthcare, physical activity detection, surveillance systems and medical fitness fields.

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A review on the artificial neural network approach to analysis and prediction of seismic damage in infrastructure

Cited by 48 publications

References 0 publications

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System

Automated Sustainable Multi-Object Segmentation and Recognition via Modified Sampling Consensus and Kernel Sliding Perceptron

Sustainable Wearable System: Human Behavior Modeling for Life-Logging Activities Using K-Ary Tree Hashing Classifier

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