scholarly journals WSN-Based Height Estimation of Moving Object in Surveillance Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Jaeseok Shim ◽  
Yujin Lim
Keyword(s):  
Surveillance System ◽  
Human Detection ◽  
Wireless Sensor ◽  
False Alarms ◽  
Surveillance Systems ◽  
Detection Mechanism ◽  
Control Center ◽  
Object Based ◽  
Measured Information ◽  
Physical Height

In the WSN- (wireless sensor network-) based surveillance system to detect undesired intrusion, all detected objects are not intruders. In order to reduce false alarms, human detection mechanism needs to determine if the detected object is a human. For human detection, physical characteristics of human are usually used. In this paper, we use the physical height to differentiate an intruder from detected objects. Using the measured information from sensors, we estimate the height of the detected object. Based on the height, if the detected object is decided as an intruder, an alarm is given to a control center. The experimental results indicate that our mechanism correctly and fast estimates the height of the object without complex computation.

Human Detection Framework for Automated Surveillance Systems

2016 ◽  
Vol 6 (2) ◽  
pp. 877
Author(s):  
Redwan A.K. Noaman ◽  
Mohd Alauddin Mohd Ali ◽  
Nasharuddin Zainal ◽  
Faisal Saeed
Keyword(s):  
Object Recognition ◽  
Surveillance System ◽  
Human Detection ◽  
Surveillance Systems ◽  
Environment Modeling ◽  
Research Attention ◽  
Area Of Interest ◽  
Edge Detecting ◽  
Object Edge ◽  
Automated Surveillance

Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas–Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system.

Human Detection by Surveillance Camera

2018 ◽  
Vol 6 (1) ◽  
pp. 21-33
Author(s):  
Larbi Guezouli ◽  
Hanane Boukhetache ◽  
Imene Kebi
Keyword(s):  
Video Surveillance ◽  
Surveillance System ◽  
Video Sequence ◽  
Rapid Development ◽  
Human Detection ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Considerable Time ◽  
Surveillance Camera ◽  
Analytical Tools

Security problems and decreasing costs, leads to the rapid development of video surveillance systems. It is necessary to implement analytical tools capable of identifying objects that may appear in the video sequence. The work presented in this article consists of designing a video surveillance system for the automatic detection of humans in a video sequence acquired by a fixed camera. The principle of this work is based on the modeling and subtraction of the background. In order to determine the nature of the objects, the authors make the detection of the contours of the foreground image, then by matching this contour with the images of a base, silhouette images of people in different positions. The acquisition of the frames is carried out in real time, the matching of the images takes a considerable time and this time becomes increasingly longer based on the size of the base. To solve this problem, the authors have used the parallelism.

scholarly journals An Event-Driven Multiple Objects Surveillance System

2018 ◽  
Vol 9 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Saeed Mina Qaisar ◽  
Dija Sidiya ◽  
Mohammad Akbar ◽  
Abdulhamit Subasi
Keyword(s):  
Power Consumption ◽  
Surveillance System ◽  
Cost Effective ◽  
Self Organization ◽  
False Alarms ◽  
Sleep Mode ◽  
Surveillance Systems ◽  
Effective Combination ◽  
Event Driven ◽  
Processing Activity

Traditional surveillance systems are constrained because of a fixed and preset pattern of monitoring. It can reduce the reliability of the system and cause an increased generation of false alarms. It results in an increased processing activity of the system, which causes an augmented consumption of system resources and power. Within this framework, a human surveillance system is proposed based on the event-driven awakening and self-organization principle. The proposed system overcomes these downsides up to a certain level. It is achieved by intelligently merging an assembly of sensors with two cameras, actuators, a lighting module and cost-effective embedded processors. With the exception of low-power event detectors, all other system modules remain in the sleep mode. These modules are activated only upon detection of an event and as a function of the sensing environment condition. It reduces power consumption and processing activity of the proposed system. An effective combination of a sensor assembly and a robust classifier suppresses generation of false alarms and improves system reliability. An experimental setup is realized in order to verify the functionality of the proposed system. Results confirm proper functionality of the implemented system. A 62.3-fold system memory utilization and bandwidth consumption reduction compared to traditional counterparts is achieved, i.e. a result of the proposed system self-organization and event-driven awakening features. It confirms that the proposed system outperforms its classical counterparts in terms of processing activity, power consumption and usage of resources

Object detection and tracking for low-cost video surveillance system

2013 ◽  
Vol 18 (2-3) ◽  
pp. 91-99
Author(s):  
Jaromir Przybylo
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Low Cost ◽  
Elderly Person ◽  
Video Stream ◽  
Modern World ◽  
False Alarms ◽  
Surveillance Systems ◽  
Unusual Behavior

Abstract Automated and intelligent video surveillance systems play important role in the modern world. Since the amount of various video streams that must be analyzed grows, such artificial intelligence systems can assist humans in performing tiresome tasks. As a result, the effectiveness of response to a dangerous situations is increasing (detect unexpected movement or unusual behavior that may pose a threat to people, property and infrastructure). Video surveillance systems have to meet several requirements: must be accurate and not produce too many false alarms, moreover it must be able to process the received video stream in real-time to provide a sufficient response time. The work presented here focuses on the selected challenges of scene analysis in video surveillance systems (object detection/tracking, effectiveness of the whole system). The aim of the research is to design a low-budget surveillance system, that can be used for example in a home security monitoring. Such solution can be use not only to surveillance but also to monitor elderly person at home or provide new ways of interacting in human-computer interaction systems.

Anomalous Event Detection Methodologies for Surveillance Application

2019 ◽  
pp. 787-813
Author(s):  
T. J. Narendra Rao ◽  
G N Girish ◽  
Mohit P. Tahiliani ◽  
Jeny Rajan
Keyword(s):  
Event Detection ◽  
Surveillance System ◽  
Visual Surveillance ◽  
Computational Time ◽  
Surveillance Systems ◽  
Detection Mechanism ◽  
Unusual Event ◽  
Anomalous Event ◽  
Insight Into ◽  
Surveillance Application

Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions. However, development of an efficient visual surveillance system is quite challenging. Designing an unusual activity detection mechanism which is accurate and real-time is the primary challenge. Review of literature carried out led to the inference that there are some attributes which are essential for a successful unusual event detection mechanism for surveillance application. The desired approach must detect genuine anomalies in real-world scenarios with acceptable accuracy, should adapt to changing environments and, should require less computational time and memory. In this chapter, an attempt has been made to provide an insight into some of the prominent approaches employed by researchers to solve these issues with a hope that it will benefit researchers towards developing a better surveillance system.

Anomalous Event Detection Methodologies for Surveillance Application

2017 ◽  
pp. 1-27
Author(s):  
T. J. Narendra Rao ◽  
G N Girish ◽  
Mohit P. Tahiliani ◽  
Jeny Rajan
Keyword(s):  
Event Detection ◽  
Surveillance System ◽  
Visual Surveillance ◽  
Computational Time ◽  
Surveillance Systems ◽  
Detection Mechanism ◽  
Unusual Event ◽  
Anomalous Event ◽  
Insight Into ◽  
Surveillance Application

Automatic visual surveillance systems serve as in-place threat detection devices being able to detect and recognize anomalous activities which otherwise would lead to potentially harmful situations, and alert the concerned authorities to take appropriate counter actions. However, development of an efficient visual surveillance system is quite challenging. Designing an unusual activity detection mechanism which is accurate and real-time is the primary challenge. Review of literature carried out led to the inference that there are some attributes which are essential for a successful unusual event detection mechanism for surveillance application. The desired approach must detect genuine anomalies in real-world scenarios with acceptable accuracy, should adapt to changing environments and, should require less computational time and memory. In this chapter, an attempt has been made to provide an insight into some of the prominent approaches employed by researchers to solve these issues with a hope that it will benefit researchers towards developing a better surveillance system.

scholarly journals Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jaehoon Jung ◽  
Inhye Yoon ◽  
Sangkeun Lee ◽  
Joonki Paik
Keyword(s):  
Error Correction ◽  
Camera Calibration ◽  
Estimation Algorithm ◽  
Human Detection ◽  
Surveillance Systems ◽  
Height Estimation ◽  
Detection And Tracking ◽  
Object Based ◽  
Human Height ◽  
Automatic Camera Calibration

This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized human height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The proposed method consists of three steps: (i) moving human detection and tracking, (ii) automatic camera calibration, and (iii) human height estimation and error correction. The proposed method automatically calibrates camera by detecting moving humans and estimates the human height using error correction. The proposed method can be applied to object-based video surveillance systems and digital forensic.

scholarly journals Human Detection Framework for Automated Surveillance Systems

2016 ◽  
Vol 6 (2) ◽  
pp. 877
Author(s):  
Redwan A.K. Noaman ◽  
Mohd Alauddin Mohd Ali ◽  
Nasharuddin Zainal ◽  
Faisal Saeed
Keyword(s):  
Object Recognition ◽  
Surveillance System ◽  
Human Detection ◽  
Surveillance Systems ◽  
Environment Modeling ◽  
Research Attention ◽  
Area Of Interest ◽  
Edge Detecting ◽  
Object Edge ◽  
Automated Surveillance

Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas–Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system.

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 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.

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