Feature selection based on Choquet integral for human activity recognition

Jarraya, Amina; Arour, Khedija; Bouzeghoub, Amel; Borgi, Amel

doi:10.1109/fuzz-ieee.2017.8015432

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…Jarraya et al selected 280 features from a total of 561 by means of a nonlinear Choquet integral feature selection approach, classified six basic actions by using the random forest, and finally obtained a better classification effect. However, the large number of selected features affected the performance of the classifier [10]. Doewes et al used the minimum redundancy and maximum correlation feature selection algorithms to analyze the number of selected features and the classification accuracy under different proportions of training sets and test sets, and considered the operation time of the classification process.…”

Section: Introductionmentioning

confidence: 99%

A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit

Fan

Jia

2019

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The purpose of activity recognition is to identify activities through a series of observations of the experimenter’s behavior and the environmental conditions. In this study, through feature selection algorithms, we researched the effects of a large number of features on human activity recognition (HAR) assisted by an inertial measurement unit (IMU), and applied them to smartphones of the future. In the research process, we considered 585 features (calculated from tri-axial accelerometer and tri-axial gyroscope data). We comprehensively analyzed the features of signals and classification methods. Three feature selection algorithms were considered, and the combination effect between the features was used to select a feature set with a significant effect on the classification of the activity, which reduced the complexity of the classifier and improved the classification accuracy. We used five classification methods (support vector machine [SVM], decision tree, linear regression, Gaussian process, and threshold selection) to verify the classification accuracy. The activity recognition method we proposed could recognize six basic activities (BAs) (standing, going upstairs, going downstairs, walking, lying, and sitting) and postural transitions (PTs) (stand-to-sit, sit-to-stand, stand-to-lie, lie-to-stand, sit-to-lie, and lie-to-sit), with an average accuracy of 96.4%.

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Section: Introductionmentioning

confidence: 99%

A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit

Fan

Jia

2019

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“…Although a lot of work has been done for basic human activity detection (walking [2][3][4][5], [8], running [10][11][12]18], sitting still [14][15][16]20] etc. [22,23,[26][27][28]); to the best of our knowledge none of these activities mentioned earlier for questionable observer detection have been detected.…”

Section: Introductionmentioning

confidence: 60%

“…Cheng et al used a dataset where five motion sensors were placed at five different parts of the body [20]. Accelerometers and gyroscopes data were used and processed for human activity recognition in different researches [23,[26][27][28]. All these works have some common problems: the users have to wear gadgets on the body which is highly impractical in real world [4]; some of them are not real time detection [4]; in the cases where the data was collected from smart phones of the users, each users wear their smart phones quite differently (some hold them in one hand, others put them in their pockets, or in their bags etc.…”

Section: Related Workmentioning

confidence: 99%

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

Anisuzzaman¹,

Saif²

2018

IJIGSP

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This research presents a framework to detect a questionable observer depending on a specific activity named "frequent iris movement". We have focused on some activities and behaviors upon which we can classify one as questionable. So this research area is not only an important part of computer vision and artificial intelligence, but also a major part of human activity recognition (HAR). We have used Haar Cascade Classifier to detect irises of both left and right eyes. Then running some morphological operation we have detected the midpoint between left and right irises; and based on some characteristics of midpoint movement we have detected a specific activityfrequent iris movement. Depending on this activity we are declaring someone as questionable observer. To validate this research we have created our own dataset with 86 videos, where 15 individuals have volunteered. We have achieved an accuracy of 90% for the first 100 frames or 3.33 seconds of each of our videos and an accuracy of 93% for the first 150 frames or 5.00 seconds of each of our videos. No work has been done yet on basis of this specific activity to detect someone as questionable and furthermore our work outperforms most of the existing work on questionable observe detection and suspicious activity recognition.

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“…Although several of research has been performed on simple human behavior detection (strolling [2][3][4][5][6], running [7][8][9][10], sit idle [11][12][13][14], etc. [15][16][17][18][19]); as far as we know, none of the behaviors listed above has been identified for dubious observer detection. Very little research has been conducted on irregular behavior identification [21] and dubious spectator detection [20,22,23] and the identification of disruptive crowd movements [24].…”

Section: Introductionmentioning

confidence: 99%

A New Framework for Video-based Frequent Iris Movement Analysis towards Anomaly Observer Detection

Rahman¹,

Cse²,

Robin³

et al. 2021

IJIGSP

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This paper suggested a new framework for detecting abnormal behavior, specifically based on frequent iris movements. It contributed to a decision whereas an individual is dubious or unsuspected from a video. One of the key components of questionable observer detection is to detect some specific suspicious activity. According to the writer, various areas of the body movement and human behaviors may be an indicator of suspicious behavior. In this research, we considered the movement of human eyes to identify suspicious activity. This working field is also a significant aspect of machine vision and artificial intelligence, and a big part of the understanding of human behavior. The system framework comprises three parts to monitor suspicious video activities. First, we used the Multi-task Cascaded Convolutional Networks (MTCNN) classifier to detect eyes. Second, we observe irises from eye representations with the use of Circular Hough Transformation (CHT). Finally, we calculated the average distance of iris movement from eye images using a new morphological method called TRM using some properties of the iris movement. We have observed a particular phenomenon of frequent iris movement. Hence, we are making a case of someone being an abnormal person and referring it to a suspicious observer. To vouch for our work, we created our data set with 100 videos where 30 individuals volunteered to validate this research. Each video comprises 200 frames with a duration of 6-10 seconds. We've reached an accuracy of 94% on detecting a frequent iris movement. Rather the goal is to minimize people's burdens so they can focus on a small range of cases for investigation in more depth. This research's sole purpose is to indicate a person's anomalous behavior on the basis of frequent iris movement. Our research outstrips much of the current literature on abnormal iris movement and dubious investigator identification.

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Feature selection based on Choquet integral for human activity recognition

Cited by 7 publications

References 17 publications

A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit

A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit

Efficient Framework Using Morphological Modeling for Frequent Iris Movement Investigation towards Questionable Observer Detection

A New Framework for Video-based Frequent Iris Movement Analysis towards Anomaly Observer Detection

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