بهینه سازی الگوریتم کاهش پس زمینه
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بهینه سازی الگوریتم کاهش پس زمینه

عنوان فارسی مقاله: بهینه سازی الگوریتم کاهش پس زمینه برای آشکارسازهای کاهش دید در شب مبتنی بر دوربین خانگی
عنوان انگلیسی مقاله: Background-Subtraction Algorithm Optimization for Home Camera-Based Night-Vision Fall Detectors
مجله/کنفرانس: دسترسی – IEEE Access
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: تشخیص کاهش، مبتنی بر دوربین، کاهش پس زمینه
کلمات کلیدی انگلیسی: Fall detection, camera-based, background-subtraction
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1109/ACCESS.2019.2948321
دانشگاه: Centre for Automation and Robotics (CAR UPM-CSIC), Universidad Politécnica de Madrid, 28012 Madrid, Spain
ناشر: آی تریپل ای - IEEE
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 4.641 در سال 2018
شاخص H_index: 56 در سال 2019
شاخص SJR: 0.609 در سال 2018
شناسه ISSN: 2169-3536
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 13
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13891
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


I. Introduction


II. Previous Work


III. Review of the Background-Subtraction Algorithms Under Analysis


IV. Description of the Fallert System


V. Methodology


Authors


Figures


References

نمونه متن انگلیسی مقاله

Abstract


Background subtraction is one of the key pre-processing steps necessary for obtaining relevant information from a video sequence. The selection of a background subtraction algorithm and its parameters is also important for achieving optimal detection performance, especially in night environments. The research contribution presented in this paper is the identification of the optimal background subtractor algorithm in indoor night-time environments, with a focus on the detection of human falls. 30 background subtraction algorithms are analyzed to determine which has the best performance in indoor night-time environments. Genetic algorithms have been applied to identify the best background subtraction algorithm, to optimize the background subtractor parameters and to calculate the optimal number of pre- and post-processing operations. The results show that the best algorithm for fall-detection in indoor, night-time environments is the LBAdaptativeSOM, optimal parameters and processing operations for this algorithm are reported.


Introduction


The risk of falling is one of the most prevalent problems faced by elderly individuals. A study published by the World Health Organization [1] estimates that between 28% and 35% of people over the age of 65 suffer at least one fall each year, and this figure increases to 42% for people over 70. According to the World Health Organization, falls represent greater than 50% of elderly hospitalizations and approximately 40% of nonnatural mortalities for this segment of the population. Falls are a significant source of mortality for elderly individuals in developed countries. Falls are particularly dangerous for people that live alone because of the amount of time that can pass before they receive assistance. Approximately onethird of the elderly (those over the age of 65) in Europe live alone [2], and the elderly population is expected to increase significantly over the next twenty years. The fall detection system proposed by Fallert [3] is based on a low-cost device comprising an embedded computer and a camera. Installed into walls or ceilings, this device monitors a room without human intervention. Thus, people monitored at home are not required to wear devices, and the system is capable of 24 h monitoring. Fallert’s fall detection system works relatively well (over 96% accuracy) during daylight, but performs poorly at night because of the lack of light. To solve this problem, the inclusion of an infrared emitter and a camera without an IR filter were required. Improvements to the background subtractor algorithm used previously [3] were required because of poor performance under night-time conditions.

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