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JSDP 2020, 17(1): 47-60 |
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Mahmoodzadeh A, Agahi H, Vaghefi M. A Fall Detection System based on the Type II Fuzzy Logic and Multi-Objective PSO Algorithm. JSDP 2020; 17 (1) :47-60
URL: http://jsdp.rcisp.ac.ir/article-1-886-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-886-en.html
Shiraz,Islamic Azad University
Abstract: (3375 Views)
The Elderly health is an important and noticeable issue; since these people are priceless resources of experience in the society. Elderly adults are more likely to be severely injured or to die following falls. Hence, fast detection of such incidents may even lead to saving the life of the injured person. Several techniques have been proposed lately for the fall detection of people, mostly categorized into three classes. The first class is based on the wearable or portable sensors [1-6]; while the second class works according to the sound or vibration sensors [7-8]. The third one is based on the machine vision. Although the latter methods require cameras and image processing systems, access to surveillance cameras -which are economical- has made them be extensively used for the elderly.
By this motivation, this paper proposes a real-time technique in which, the surveillance video frames of the person’s room are being processed. This proposed method works based on the feature extraction and applying type-II fuzzy algorithm for the fall detection. First, using the improved visual background extraction (ViBe) algorithm, pixels of the moving person are separated from those of the background. Then, using the obtained image for the moving person, six features including ‘aspect ratio’, ‘motion vector’, ‘center-of-gravity’, ‘motion history image’, ‘the angle between the major axis of the bounding ellipse and the horizontal axis’ and the ‘ratio of major axis to minor axis of the bounding ellipse’ are extracted. These features should be given to an appropriate classifier.
In this paper, an interval type-II fuzzy logic system (IT2FLS) is utilized as the classifier. To do this, three membership functions are considered for each feature. Accordingly, the number of the fuzzy laws for six features is too large, leading to high computational complexity. Since most of these laws in the fall detection are irrelevant or redundant, an appropriate algorithm is used to select the most effective fuzzy membership functions. The multi-objective particle swarm optimization algorithm (MOPSO) is an operative tool for solving large-scale problems. In this paper, this evolutionary algorithm tries to select the most effective membership functions to maximize the ‘classification accuracy’ while the ‘number of the selected membership functions’ are simultaneously minimized. This results in a considerably smaller number of rules.
In this paper to investigate the performance of the proposed algorithm, 136 videos from the movements of people were produced; among which 97 people fell down and 39 ones were related to the normal activities (non-fall). To this end, three criteria including accuracy (ACC), sensitivity (Se.), and specificity (Sp.) are used. By changing the initial values of the parameters of the ViBe algorithm and frequent re-tuning after multiple frames, detecting the moving objects is done faster and with higher robustness against noise and illumination variations in the environment. This can be done via the proposed system even in microprocessors with low computational power. The obtained results of applying the proposed approach confirmed that this system is able to detect the human fall quickly and precisely.
By this motivation, this paper proposes a real-time technique in which, the surveillance video frames of the person’s room are being processed. This proposed method works based on the feature extraction and applying type-II fuzzy algorithm for the fall detection. First, using the improved visual background extraction (ViBe) algorithm, pixels of the moving person are separated from those of the background. Then, using the obtained image for the moving person, six features including ‘aspect ratio’, ‘motion vector’, ‘center-of-gravity’, ‘motion history image’, ‘the angle between the major axis of the bounding ellipse and the horizontal axis’ and the ‘ratio of major axis to minor axis of the bounding ellipse’ are extracted. These features should be given to an appropriate classifier.
In this paper, an interval type-II fuzzy logic system (IT2FLS) is utilized as the classifier. To do this, three membership functions are considered for each feature. Accordingly, the number of the fuzzy laws for six features is too large, leading to high computational complexity. Since most of these laws in the fall detection are irrelevant or redundant, an appropriate algorithm is used to select the most effective fuzzy membership functions. The multi-objective particle swarm optimization algorithm (MOPSO) is an operative tool for solving large-scale problems. In this paper, this evolutionary algorithm tries to select the most effective membership functions to maximize the ‘classification accuracy’ while the ‘number of the selected membership functions’ are simultaneously minimized. This results in a considerably smaller number of rules.
In this paper to investigate the performance of the proposed algorithm, 136 videos from the movements of people were produced; among which 97 people fell down and 39 ones were related to the normal activities (non-fall). To this end, three criteria including accuracy (ACC), sensitivity (Se.), and specificity (Sp.) are used. By changing the initial values of the parameters of the ViBe algorithm and frequent re-tuning after multiple frames, detecting the moving objects is done faster and with higher robustness against noise and illumination variations in the environment. This can be done via the proposed system even in microprocessors with low computational power. The obtained results of applying the proposed approach confirmed that this system is able to detect the human fall quickly and precisely.
Type of Study: Research |
Subject:
Paper
Received: 2018/08/14 | Accepted: 2019/09/2 | Published: 2020/06/21 | ePublished: 2020/06/21
Received: 2018/08/14 | Accepted: 2019/09/2 | Published: 2020/06/21 | ePublished: 2020/06/21
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