Automated multi-feature human interaction recognition in complex environment

Bibi, Shafina; Anjum, Nadeem; Sher, Muhammad

doi:10.1016/j.compind.2018.03.015

Cited by 17 publications

(5 citation statements)

References 42 publications

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“…In this system, some confusion was observed due to the similarities in angles and positions of various actions. Furthermore, Bibi et al [10] proposed an HIR system with local binary patterns using multi-view cameras. A high confusion rate was observed in similar interactions.…”

Section: Hhi Recognition Systemsmentioning

confidence: 99%

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Ghadi¹,

Khalid²,

Alsuhibany³

et al. 2022

Computers, Materials &Amp; Continua

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Human Activity Recognition (HAR) plays an important role in life care and health monitoring since it involves examining various activities of patients at homes, hospitals, or offices. Hence, the proposed system integrates Human-Human Interaction (HHI) and Human-Object Interaction (HOI) recognition to provide in-depth monitoring of the daily routine of patients. We propose a robust system comprising both RGB (red, green, blue) and depth information. In particular, humans in HHI datasets are segmented via connected components analysis and skin detection while the human and object in HOI datasets are segmented via saliency map. To track the movement of humans, we proposed orientation and thermal features. A codebook is generated using Linde-Buzo-Gray (LBG) algorithm for vector quantization. Then, the quantized vectors generated from image sequences of HOI are given to Artificial Neural Network (ANN) while the quantized vectors generated from image sequences of HHI are given to K-ary tree hashing for classification. There are two publicly available datasets used for experimentation on HHI recognition: Stony Brook University (SBU) Kinect interaction and the University of Lincoln's (UoL) 3D social activity dataset. Furthermore, two publicly available datasets are used for experimentation on HOI recognition: Nanyang Technological University (NTU) RGB-D and Sun Yat-Sen University (SYSU) 3D HOI datasets. The results proved the validity of the proposed system.

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Section: Hhi Recognition Systemsmentioning

confidence: 99%

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Ghadi¹,

Khalid²,

Alsuhibany³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In skeleton point modeling, firstly, the contours of a human silhouette have been calculated that detect the outer pixels of human shape [30]. Secondly, torso has been traced by approximating the center point of calculated human contours [31], which is depicted in Eq.…”

Section: Skeleton Point Modelingmentioning

confidence: 99%

Self-Care Assessment for Daily Living Using Machine Learning Mechanism

Batool¹,

Ghadi²,

Alsuhibany³

et al. 2022

Computers, Materials &Amp; Continua

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Nowadays, activities of daily living (ADL) recognition system has been considered an important field of computer vision. Wearable and optical sensors are widely used to assess the daily living activities in healthy people and people with certain disorders. Although conventional ADL utilizes RGB optical sensors but an RGB-D camera with features of identifying depth (distance information) and visual cues has greatly enhanced the performance of activity recognition. In this paper, an RGB-D-based ADL recognition system has been presented. Initially, human silhouette has been extracted from the noisy background of RGB and depth images to track human movement in a scene. Based on these silhouettes, full body features and point based features have been extracted which are further optimized with probability based incremental learning (PBIL) algorithm. Finally, random forest classifier has been used to classify activities into different categories. The n-fold crossvalidation scheme has been used to measure the viability of the proposed model on the RGBD-AC benchmark dataset and has achieved an accuracy of 92.71% over other state-of-the-art methodologies.

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“…Lee et al used stack self-coding network for speech feature coding and compressed the data to a preset length with minimum reconstruction error [10]. Bibi et al have explored various DL (deep learning) frameworks in speech emotion tasks, and their experiments have demonstrated that feedforward and RNN (recurrent neural network) structures and their variants can be used to assist speech recognition, especially emotion recognition [11]. Fornaser et al put forward a method of identifying isolated sound events by using DBN (deep belief network).…”

Section: Mnfr-related Research Nazemi Et Al Comprehensivelymentioning

confidence: 99%

Musical Note Feature Recognition Based on Long Short-Term Memory

Yuan

2022

Mobile Information Systems

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The timbre and volume of a single tone are among its fundamental characteristics. The single-tone detection technology is the key to the foundation of MNFR (musical note feature recognition), which is built on the fundamental feature extraction of single tones. A MNFR method based on LSTM (long short-term memory) is proposed because traditional methods have low accuracy in note feature classification and low accuracy in MNFR. To process the series of convolution feature maps, the feature maps are directly input into LSTM to learn hash codes. Extract the note features. Segment according to the changing trend of the physical features of the notes. Additionally, a number of feature maps are built from convolution feature maps extracted from numerous convolution layers of previously trained CNNs (convolutional neural networks), taking into account the spatial specifics and semantic features. The note start vector is produced using the enhanced peak extraction algorithm based on Gaussian kernel smoothing. The findings indicate that, when 100 samples are used, this method’s note classification accuracy differs from that of the DBN (deep belief network) and DWT (discrete wavelet transform) by 1.17 percent and 2.04 percent, respectively. The analysis in the conclusion demonstrates that the algorithm put forth in this paper is both theoretically and practically workable.

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Automated multi-feature human interaction recognition in complex environment

Cited by 17 publications

References 42 publications

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

An Intelligent HealthCare Monitoring Framework for Daily Assistant Living

Self-Care Assessment for Daily Living Using Machine Learning Mechanism

Musical Note Feature Recognition Based on Long Short-Term Memory

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