Video Surveillance System for Elderly Person Living Alone by Person Tracking and Fall Detection

2006 ◽  
Vol 126 (8) ◽  
pp. 457-463 ◽  
Author(s):  
Motonori Doi ◽  
Hiroshi Inoue ◽  
Yutaro Aoki ◽  
Osamu Oshiro
2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Chih Chen ◽  
Yang-Ming Liu

This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extracted, whether or not the posture has changed is evaluated using the aspect ratio and height of a human body. Meanwhile, the proposed system detects a change of the posture and extracts the histogram of the object projection to represent the appearance. The histogram becomes the input vector of K-Nearest Neighbor (K-NN) algorithm and is to evaluate the posture of the object. Capable of accurately detecting different postures of a human, the proposed system increases the fall detection accuracy. Importantly, the proposed method detects the posture using the frame ratio and the displacement of height in an image. Experimental results demonstrate that the proposed system can further improve the system performance and the fall down identification accuracy.


Author(s):  
Koudai Yano ◽  
Yusuke Manabe ◽  
Masatsugu Hirano ◽  
Kohei Ishii ◽  
Mikio Deguchi ◽  
...  

2016 ◽  
Vol 26 (04) ◽  
pp. 1750056
Author(s):  
Chao Tong ◽  
Yu Lian ◽  
Yang Zhang ◽  
Zhongyu Xie ◽  
Xiang Long ◽  
...  

In recent years, due to the growing population of the elderly, falls of elderly people have aroused wide public concern. Detecting timely falls of the elderly is significant to their safety. Numerous challenges exist in real-time fall detection systems because some features of normal human activities are greatly similar to the characteristics of falls. To address these problems, we propose a novel fall detection scheme and build a health-care system to detect falls of the elderly based on a real-time video surveillance system and a smart phone. The system contains two major modules. The first module is a feature extraction module. We adopt the Gaussian mixture model, tracking learning detecting algorithm and logpolar histogram to extract the characteristics of falls from the video surveillance system and the sensors embedded in mobile phones. The main purpose of the second module is to detect a fall-based on the features obtained in the first module. The experimental results show that every module is significant. Besides, our system is effective to separate falls from other similar actions such as bend down with an accuracy rate of more than 98% and performs better than other state-of-the-art fall detection systems.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jin Su Kim ◽  
Min-Gu Kim ◽  
Sung Bum Pan

AbstractConventional 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.


2007 ◽  
Vol 33 (2) ◽  
pp. 179-184 ◽  
Author(s):  
Panagiotis Dendrinos ◽  
Eleni Tounta ◽  
Alexandros A. Karamanlidis ◽  
Anastasios Legakis ◽  
Spyros Kotomatas

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi

In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.


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