A method of detecting human body falling action in a complex background

Jia, Dongfang; Groza, V.A.; Liu, Xiaohui; Liu, Xu; Huaihua, Zhu; Zeng, Qingsheng

doi:10.1109/ines.2016.7555148

Cited by 2 publications

(1 citation statement)

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“…The method for using images is divided into the method for setting up a circle changing depending on object motion, and using circle changes, vertical and horizontal histogram feature values to determine fall with SVM [29], the method for setting up a bounding box in a detected object and using changing acceleration of the bounding box [30], and the method for using aspect ratios, effective area ratios of a concerned object, object feature points, axis angles, and contour ratios to determine fall with SVM [31].…”

Section: Abnormal Situation Detection Algorithmmentioning

confidence: 99%

A study on implementation of real-time intelligent video surveillance system based on embedded module

Kim

Pan

2021

J Image Video Proc.

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Conventional surveillance systems for preventing accidents and incidents do not identify 95% thereof after 22 min when one person monitors a plurality of closed circuit televisions (CCTV). To address this issue, while computer-based intelligent video surveillance systems have been studied to notify users of abnormal situations when they happen, it is not commonly used in real environment because of weakness of personal information leaks and high power consumption. To address this issue, intelligent video surveillance systems based on small devices have been studied. This paper suggests implement an intelligent video surveillance system based on embedded modules for intruder detection based on information learning, fire detection based on color and motion information, and loitering and fall detection based on human body motion. Moreover, an algorithm and an embedded module optimization method are applied for real-time processing. The implemented algorithm showed performance of 88.51% for intruder detection, 92.63% for fire detection, 80% for loitering detection and 93.54% for fall detection. The result of comparison before and after optimization about the algorithm processing time showed 50.53% of decrease, implying potential real-time driving of the intelligent image monitoring system based on embedded modules.

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Section: Abnormal Situation Detection Algorithmmentioning

confidence: 99%