scholarly journals Statistical accuracy analysis of different detecting algorithms for surveillance system in smart city

2020 ◽  
Vol 18 (2) ◽  
pp. 979
Author(s):  
Hassan Al-Yassin ◽  
Jaafar I. Mousa ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Laith Alzubaidi
Keyword(s):  
False Recognition ◽  
Surveillance System ◽  
Smart Cities ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Surveillance Systems ◽  
Accuracy Analysis ◽  
Statistical Accuracy ◽  
Temporal Differencing ◽  
Fragmentation Ratio

<span>Several detecting algorithms are developed for real-time surveillance systems in the smart cities. The most popular algorithms due to its accuracy are: Temporal Differencing, Background Subtraction, and Gaussian Mixture Models. Selecting of which algorithm is the best to be used, based on accuracy, is a good choise, but is not the best. Statistical accuracy anlysis tests are required for achieving a confident decision. This paper presents further analysis of the accuracy by employing four parameters: false recognition, unrecognized, true recognition, and total fragmentation ratios. The results proof that no algorithm is selected as the perfect or suitable for all applications based on the total fragmentation ratio, whereas both false recognition ratio and unrecognized ratio parameters have a significant impact. The mlti-way Analysis of Variate (so-called K-way ANONVA) is used for proofing the results based on SPSS statistics.</span>

scholarly journals Secure Video Surveillance Framework in Smart City

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4419
Author(s):  
Hao Li ◽  
Tianhao Xiezhang ◽  
Cheng Yang ◽  
Lianbing Deng ◽  
Peng Yi
Keyword(s):  
Video Surveillance ◽  
Smart City ◽  
Key Management ◽  
Surveillance System ◽  
Chinese Remainder Theorem ◽  
Smart Cities ◽  
Group Key Management ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Key Update

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.

An Intelligent Video Surveillance System for Android Smart Phone

2013 ◽  
Vol 850-851 ◽  
pp. 884-888 ◽  
Author(s):  
Gang Yang ◽  
Xin Tan ◽  
Yong Rui Zhang
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Surveillance System ◽  
Smart Phone ◽  
Image Data ◽  
Gaussian Mixture ◽  
Moving Object ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Surveillance Technology

Video surveillance technology is playing an important role, and it is widely used in some fields. With the popularity of Android OS, it draws researchers attention to increase the development of video surveillance systems on the platform. This paper presents a smart real-time video surveillance system based on Android smart phone. This system detects moving object by using improved GMM (Gaussian Mixture Mode) algorithm, recognizes invading human with cascade classifier, processes image data with coder & decoder, transmits data over RTP (Real-time Transport Protocol). It also applies some methods to improve the accuracy of moving object detection and recognition, speed up recognition process. The experimental evidences show that it can realize real-time video surveillance and smart alarm.

scholarly journals Suspicious Action Detection in Intelligent Surveillance System Using Action Attribute Modelling

2021 ◽  
Author(s):  
Manisha Mudgal ◽  
Deepika Punj ◽  
Anuradha Pillai
Keyword(s):  
Image Processing ◽  
Surveillance System ◽  
Intelligent System ◽  
Gaussian Mixture ◽  
Human Action ◽  
Surveillance Systems ◽  
Railway Stations ◽  
Intelligent Surveillance ◽  
Attribute Model ◽  
Low Dimensional

Research in the field of image processing and computer vision for recognition of suspicious activity is growing actively.   Surveillance systems play a key role in monitoring of sensitive places such as airports, railway stations, shopping complexes,   roads, parking areas, roads, banks. For a human it is very difficult to monitor surveillance videos continually, therefore a smart and intelligent system is required that can do real time monitoring of all activities and can categories between usual and some abnormal activities. In this paper many different abnormal activities has been discussed.  More focuses is given to violence activity like hitting, slapping, punching etc. For this large human action dataset like UCF101, Kaggel is required. This paper proposes a method to model violence actions using Gaussian Mixture Model with Universal Attribute Model. In this action vector is used to remove redundant attributes and get a low dimensional relevant action vectors.

Derivation of New Surveillance System for Influenza-like Illness Using Claims Data 2014-2018 in Korea (Preprint)

2020 ◽  
Author(s):  
HeeKyung Choi ◽  
Won Suk Choi ◽  
Euna Han
Keyword(s):  
Surveillance System ◽  
Claims Data ◽  
Health Concern ◽  
Influenza Surveillance ◽  
Utilization Review ◽  
Surveillance Systems ◽  
Influenza Like Illness ◽  
System P ◽  
National Patient ◽  
Antitussive Agents

BACKGROUND Influenza is an important public health concern. A national surveillance system that easily and rapidly detects influenza epidemics is lacking. OBJECTIVE We assumed that the rate of influenza-like illness (ILI) related-claims is similar to the current ILI surveillance system. METHODS We used the Health Insurance Review and Assessment Service-National Patient Samples (HIRA-NPS), 2014-2018. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims. We compared ILI-related claims and weekly ILI rates from clinical sentinel surveillance data. RESULTS We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the four-years were 84.64 and 86.19 cases claims per 1,000 claims for claims data and 12.27 and 16.82 per 1,000 patients for sentinel data (Figure 5). Both the claims and sentinel data surpassed the epidemic thresholds each season. The peak epidemic in the claims data was reached one to two weeks later than in the sentinel data. The epidemic patterns were more similar in the 2016-2017 and 2017-2018 seasons than the 2014-2015 and 2015-2016 seasons. CONCLUSIONS Based on hospital reports, ILI-related claims rates were similar to the ILI surveillance system. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.

scholarly journals Surveillance System in Smart Cities: A Dependability Evaluation Based on Stochastic Models

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 876
Author(s):  
Igor Gonçalves ◽  
Laécio Rodrigues ◽  
Francisco Airton Silva ◽  
Tuan Anh Nguyen ◽  
Dugki Min ◽  
...  
Keyword(s):  
Surveillance System ◽  
Smart Cities ◽  
Heterogeneous Data ◽  
Time To Failure ◽  
Video Streams ◽  
Dependability Evaluation ◽  
The Mean ◽  
Mean Time ◽  
The Internet Of Things

Surveillance monitoring systems are highly necessary, aiming to prevent many social problems in smart cities. The internet of things (IoT) nowadays offers a variety of technologies to capture and process massive and heterogeneous data. Due to the fact that (i) advanced analyses of video streams are performed on powerful recording devices; while (ii) surveillance monitoring services require high availability levels in the way that the service must remain connected, for example, to a connection network that offers higher speed than conventional connections; and that (iii) the trust-worthy dependability of a surveillance system depends on various factors, it is not easy to identify which components/devices in a system architecture have the most impact on the dependability for a specific surveillance system in smart cities. In this paper, we developed stochastic Petri net models for a surveillance monitoring system with regard to varying several parameters to obtain the highest dependability. Two main metrics of interest in the dependability of a surveillance system including reliability and availability were analyzed in a comprehensive manner. The analysis results show that the variation in the number of long-term evolution (LTE)-based stations contributes to a number of nines (#9s) increase in availability. The obtained results show that the variation of the mean time to failure (MTTF) of surveillance cameras exposes a high impact on the reliability of the system. The findings of this work have the potential of assisting system architects in planning more optimized systems in this field based on the proposed models.

scholarly journals Measure of Similarity between GMMs by Embedding of the Parameter Space That Preserves KL Divergence

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 957
Author(s):  
Branislav Popović ◽  
Lenka Cepova ◽  
Robert Cep ◽  
Marko Janev ◽  
Lidija Krstanović
Keyword(s):  
Computational Complexity ◽  
Parameter Space ◽  
Recognition Accuracy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Dimensional Manifold ◽  
Dimensional Parameter ◽  
Trade Off ◽  
Measure Of Similarity ◽  
Lower Dimensional

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

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