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

Rafique, Adnan Ahmed; Jalal, Ahmad; Kim, Kibum

doi:10.3390/sym12111928

Cited by 55 publications

(15 citation statements)

References 66 publications

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“…The separation of the fingers is represented as open, closed, crossed, and semi-open. By these notations, the set of features is optimized, helping to reduce the overall computational time and complexity [60][61][62][63]. Figure 8 shows the result of the fuzzy logic optimization.…”

Section: Feature Foptimization Using Fuzzy Logicmentioning

confidence: 99%

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

et al. 2022

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In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.

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Section: Feature Foptimization Using Fuzzy Logicmentioning

confidence: 99%

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

et al. 2022

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“…This paper proposes three features for IMU signals: 1D-LBP, state-space correlation entropy (SSCE), and dispersion entropy (DE), which is explained in the sections below. Algorithm 2 ( 1 SSCE and 2 Dispersion Entropy [42][43][44]) in the supplementary materials section shows the pseudocode for the overall IMU feature extraction. [45] technique.…”

Section: Imu-based Hybrid Feature Extractionmentioning

confidence: 99%

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

2021

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The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

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“…Activity recognition by digital monitoring systems is useful in many daily life applications such as video indexing and retrieval, virtual worlds and surveillance systems installed in houses, hospitals and public areas [12][13][14]. An automatic, efficient and robust surveillance system is imperative because of the elevated crime rates all over the world [15,16].…”

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

Cited by 55 publications

References 66 publications

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

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

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