Early detection of human actions—A hybrid approach

Vats, Ekta; Chan, Chee Seng

doi:10.1016/j.asoc.2015.11.007

Cited by 19 publications

(9 citation statements)

References 25 publications

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“…where is 1 if the class of x nk M n is k. The parameter w n represents the positive weight of the data. The covariance matrix is calculated according to (9).…”

Section: B Fuzzy Integral Based Classifier Fusionmentioning

confidence: 99%

“…In (9), is the sum of the weights for class k and is the sum of the squared weights for each class. LDA uses the prior probability, posterior probability, and cost value.…”

Section: B Fuzzy Integral Based Classifier Fusionmentioning

confidence: 99%

“…For the early detection of human actions, a hybrid approach has been proposed [9]. The proposed method combines computer vision with fuzzy set theory.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2018

Adv. Electr. Comp. Eng.

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The human activity recognition is an important issue for sports analysis and health monitoring. The early recognition of human actions is used in areas such as detection of criminal activities, fall detection, and action recognition in rehabilitation centers. Especially, the detection of the falls in elderly people is very important for rapid intervention. Mobile phones can be used for action recognition with their built-in accelerometer sensor. In this study, a new combined method based on fuzzy integral and cuckoo search is proposed for classifying human actions. The signals are acquired from three axes of acceleration sensor of a mobile phone and the features are extracted by applying signal processing methods. Our approach utilizes from linear discriminant analysis (LDA), support vector machines (SVM), and neural networks (NN) techniques and aggregates their outputs by using fuzzy integral. The cuckoo search method adjusts the parameters for assignment of optimal confidence levels of the classifiers. The experimental results show that our model provides better performance than the individual classifiers. In addition, appropriate selection of the confidence levels improves the performance of the combined classifiers.

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“…where is 1 if the class of x nk M n is k. The parameter w n represents the positive weight of the data. The covariance matrix is calculated according to (9).…”

Section: B Fuzzy Integral Based Classifier Fusionmentioning

confidence: 99%

“…In (9), is the sum of the weights for class k and is the sum of the squared weights for each class. LDA uses the prior probability, posterior probability, and cost value.…”

Section: B Fuzzy Integral Based Classifier Fusionmentioning

confidence: 99%

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Untitled

2018

Adv. Electr. Comp. Eng.

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“…Novel applications of human motion analysis have been reviewed (Lim et al, 2015), including motion detection (Candamo et al, 2010) and identification of common events. Examples of studies of common movements or actions include walking, running, waving, jumping (Vats and Chan, 2016) and recognition of the quality of movements, such as human gait characterisation (Paul et al, 2013) or angry postures in crowds (Gilbert et al, 2011). Activity analysis has been described as the area with the greatest potential for research development (Popoola and Wang, 2012).…”

Section: Surveillance Of Behaviours In Rail Security Contextsmentioning

confidence: 99%

Developing a framework of behaviours before suicides at railway locations

Ryan

2017

Ergonomics

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Better knowledge of behaviours of people at railway property could help with identifying those at risk of suicide. Literature has been reviewed from a range of disciplines on what is known about studying behaviour in this type of public location. Secondary analysis has been carried out on descriptions of behaviour from structured exercises with experts and other pre-existing sources. A framework has been produced with five main classes (display of emotion, appearance, posture/movements, activities and interactions) and associated sub-classes. Commentary has been provided on factors that influence identification of suspicious behaviours, how to distinguish these from normal behaviours and the circumstances that inhibit timely reactions to the behaviour amidst the complexity of the operational railway. Opportunities to develop and use the framework are discussed, including using this to prompt collection of additional behavioural data from wider resources, enhancing staff training and developing requirements for effective use of surveillance technologies. Practitioner Summary: Many railway suicides could be prevented with better understanding of behaviours before events. Pre-existing data sources have been analysed, producing a framework highlighting five aspects of behaviour. This can prompt the collection of better evidence on pre-suicidal behaviours, with future applications in developing surveillance technologies, training staff and public awareness.

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“…Artificial intelligence, Artificial Neural Network, Fuzzy logic and expert systems have been increasingly used in various applications in the last 30 years: Engineering design, Image recognition [2]; Prediction, Estimation, Pattern recognition, and optimization [3]; Petroleum exploration and production; civil engineering, environmental and water resources engineering, traffic engineering, highway engineering, geotechnical engineering [4]; Image classification [5]; Fingerprint analysis [6]; Software defect prediction [7]; Breast cancer identification [8]; Human action recognition, video surveillance to health-care [9]; Video retrieval [10]; Localization scheme of wireless sensor networks, military surveillance, environmental monitoring, robotics, domestics, animal tracking [11]; Image recognition of plant diseases [12]; Wind power forecasting [13]; Design and analysis of antennas [14]; Image recognition [15]; Multimodal medical image fusion [16]; Satellite data and GPS [17]; water resources engineering [18]; air traffic control [19]; financial forecasting [20]; earthquake prediction [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

Narayanakumar¹,

Raja²

2016

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The aim of this study is to evaluate the performance of BP neural network techniques in predicting earthquakes occurring in the region of Himalayan belt (with the use of different types of input data). These parameters are extracted from Himalayan Earthquake catalogue comprised of all minor, major events and their aftershock sequences in the Himalayan basin for the past 128 years from 1887 to 2015. This data warehouse contains event data, event time with seconds, latitude, longitude, depth, standard deviation and magnitude. These field data are converted into eight mathematically computed parameters known as seismicity indicators. These seismicity indicators have been used to train the BP Neural Network for better decision making and predicting the magnitude of the pre-defined future time period. These mathematically computed indicators considered are the clustered based on every events above 2.5 magnitude, total number of events from past years to 2014, frequencymagnitude distribution b-values, Gutenberg-Richter inverse power law curve for the n events, the rate of square root of seismic energy released during the n events, energy released from the event, the mean square deviation about the regression line based on the Gutenberg-Richer inverse power law for the n events, coefficient of variation of mean time and average value of the magnitude for last n events. We propose a three-layer feed forward BP neural network model to identify factors, with the actual occurrence of the earthquake magnitude M and other seven mathematically computed parameters seismicity indicators as input and target vectors in Himalayan basin area. We infer through comparing curve as observed from seismometer in Himalayan Earthquake catalogue comprised of all events above magnitude 2.5 mg, their aftershock sequences in the Himalayan basin of year 2015 and BP neural network predicting earthquakes in 2015. The model yields good prediction result for the How to cite this paper: Narayanakumar, S. and Raja, K.

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Early detection of human actions—A hybrid approach

Cited by 19 publications

References 25 publications

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Developing a framework of behaviours before suicides at railway locations

A BP Artificial Neural Network Model for Earthquake Magnitude Prediction in Himalayas, India

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