Violent actions against children

Alhammami, Muhammad; Ooi, Chee‐Pun; Tan, Wooi-Haw

doi:10.1016/j.dib.2017.04.026

Cited by 5 publications

(9 citation statements)

References 3 publications

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“…To build VACR hardware IP, the methodology customizes an existing machine-learning based model for detecting violence against children in [1 , 2] by reducing the number of required features to fit the structure of resources of the used FPGA device. It also uses the MMU VAAC dataset [4] developed by the Digital Home Research Centre at Multimedia University.…”

Section: Methods Detailsmentioning

confidence: 99%

An FPGA-based IP for recognizing violence against children

Alhammami

Hammami

2021

MethodsX

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We present in this paper an FPGA-based IP for recognizing most common violent actions against children (VACR IP). VACR IP uses only skeleton joints data as inputs to keep the privacy of people inside their homes or in the schools. The proposed hardware achieved a detection rate of 97.72%, processing speed 761FPS, and a latency value equals to 2.79msec using a 50MHz system clock. In sum, this research method presents: First FPGA-based IP for recognizing most common child abuses without any privacy breach of people real images by using only skeleton joints data. The IP can detect the violence and the type of the violent action. The IP can be embedded in complete systems to be installed in schools by school psychologists and counselors.

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Section: Methods Detailsmentioning

confidence: 99%

An FPGA-based IP for recognizing violence against children

Alhammami

Hammami

2021

MethodsX

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“…The utilization of the mental healthcare services in the demographics of the study area in particular and Nigeria in general are historically low due to long distance, unavailability of medications, stigmatization, epileptic or skeletal services, poor road networks, poverty and dearth of skilled psychiatrics [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] . Generally, the following statistical analysis and survey methods in these articles can be useful [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] .…”

Section: Experimental Design Materials and Methodsmentioning

confidence: 99%

Survey datasets on patterns of utilization of mental healthcare services among people living with mental illness

et al. 2018

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The data was obtained from a field survey aimed at measuring the patterns of utilization of mental healthcare services among people living with mental illness. The data was collected using a standardized and structured questionnaire from People Living with Mental Illness (PLMI) receiving treatment and the care-givers of People Living with Mental Illness. Three psychiatric hospitals in Ogun state, Nigeria were the population from which the samples were taken. Chi-square test of independence and correspondence analysis were used to present the data in analyzed form.

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“…This research uses the MMU VAAC dataset containing adult  child actions. The recorded actions include kicking, punching, throwing, shoving, strangling and slapping, touching, hugging, carrying, laying down, departing, and approaching [21].…”

Section: Skeletal Based Datasets Of Human-human Actionsmentioning

confidence: 99%

“…1 illustrates the research process. While creating the dataset has been discussed in [21], skeleton tracking will be done using the depth sensor. The research determines the suitable features and classifier for this model is performed once offline during the features extraction and classification phases.…”

Section: The Recognition Modelmentioning

confidence: 99%

Optimised ML-based System Model for Adult-Child Actions Recognition

Alhammami¹,

Hammami²,

Ooi³

et al. 2019

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Many critical applications require accurate real-time human action recognition. However, there are many hurdles associated with capturing and pre-processing image data, calculating features, and classification because they consume significant resources for both storage and computation. To circumvent these hurdles, this paper presents a recognition machine learning (ML) based system model which uses reduced data structure features by projecting real 3D skeleton modality on virtual 2D space. The MMU VAAC dataset is used to test the proposed ML model. The results show a high accuracy rate of 97.88% which is only slightly lower than the accuracy when using the original 3D modality-based features but with a 75% reduction ratio from using RGB modality. These results motivate implementing the proposed recognition model on an embedded system platform in the future.

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Violent actions against children

Abstract: We present in this paper a novel dataset (MMU VAAC) for violent actions against children recognition. This original dataset has been recorded using Microsoft Kinect with the usage of a child mannequin. MMU VAAC dataset contains skeleton joints, depth, and RGB modalities.

Cited by 5 publications

References 3 publications

An FPGA-based IP for recognizing violence against children

An FPGA-based IP for recognizing violence against children

Survey datasets on patterns of utilization of mental healthcare services among people living with mental illness

Optimised ML-based System Model for Adult-Child Actions Recognition

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