Fuzzy ontology-based complex and uncertain video surveillance events recognition

Nowadays, most people can get enough energy to maintain one-day activity, while few people know whether they eat healthily or not. It is quite important to analyze nutritional facts of foods eaten for those who are losing weight or suffering chronic diseases such as diabetes. However, diet is a problem with a high uncertainty, and it is widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like diet. On the other hand, a fuzzy ontology can effectively help handle and process uncertain data and knowledge. This chapter proposes a type-2 fuzzy set and fuzzy ontology for diet application and uses the type-2 fuzzy markup language (T2FML) to describe the knowledge base and rule base of the diet, including ingredients and the contained servings of six food categories of some common foods in Taiwan. The experimental results show that type-2 fuzzy logic system (FLS) performs better than type-1 FLS, proving that type-2 FLS can provide a powerful paradigm to handle the high level of uncertainties present in diet.

Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model''''s performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.

A major challenge that all law-enforcement and intelligence-gathering organizations are facing is to accurately and efficiently analyze the growing volumes of crime data. In many fields, video surveillance can be used for that purpose. Video surveillance systems are now commonly used at various places like banks, hotels, schools, supermarkets. These systems are applicable for real-time monitoring or post checking. Current video surveillance systems have lower intelligence and it need people to monitor them. Today, video surveillance systems are used for traffic cameras, too. Traffic cameras are an innovative and extremely functional application of video surveillance technology. Whether they are recording traffic patterns for the future study and observation or monitoring traffic and issuing tickets for moving violations, traffic cameras are an explosively popular form of video surveillance. One way to find traffic patterns or to prevent traffic accidents and provide better road security is to use data mining techniques. With data mining, stream from many traffic cameras can be processed in real time. Property like this could provide some intelligence upgrade within systems, so they could make decision on their own without the need of people to monitor them. In this paper the authors described the data mining techniques for stream processing, pattern and abnormality detection in traffic surveillance systems. Each video recording and the use of recorded video on the court must be covered by legal regulations. The authors investigated and extracted the appropriate articles from concrete Laws of Republic Serbia, covering this kind of problems.

Use of video surveillance has substantially increased in the last few decades. Modern video surveillance systems are equipped with techniques that allow traversal of data in an effective and efficient manner, giving massive powers to operators and potentially compromising the privacy of anyone observed by the system. Several techniques to protect the privacy of individuals have therefore been proposed, but very little research work has focused on the specific security requirements of video surveillance data (in transit or in storage) and on authorizing access to this data. In this paper, we present a general model of video surveillance systems that will help identify the major security and privacy requirements for a video surveillance system and we use this model to identify practical challenges in ensuring the security of video surveillance data in all stages (in transit and at rest). Our study shows a gap between the identified security requirements and the proposed security solutions where future research efforts may focus in this domain.KeywordsVideo SurveillanceSecurityPrivacyMonitoringStorageAccess ControlEncryption

Search for criminals becomes more difficult in modern conditions of continuous growth of the population of big urban centers and more and more frequent use of the latest achievements of science and technology by criminals for the realization of their purposes and for counteraction to law enforcement agencies. To increase the efficiency of the search for offenders it is necessary to introduce the latest technologies. Therefore, the systems of video recording and video surveillance, which currently operate in Ukraine and are one of the areas of using these technologies, have been studied. It has been noted that the installed CCTV cameras mostly work in manual mode. The employee of the information processing center must watch the video in order to gather evidence and search for suspects. It takes a lot of time and requires the involvement of a large number of staff, which makes the search for criminals and obtaining evidence from video surveillance systems ineffective.
 Tendencies in the development of intelligent video surveillance systems in developed countries have been analyzed. It has been found out that such countries (EU, USA, China, Russia) install modern “smart” CCTV cameras, the information from which is sent to modern situational centers, where it is processed by using artificial intelligence, neural networks and cloud infrastructure. Certain types of cameras even have the ability to independently process the received information. This makes it possible to perform biometric facial recognition, to detect behavioral abnormalities of suspects who intend to commit offenses; to record the appearance and disappearance of objects, to recognize the state car plates, to recognize their type, brand and color, to record the way of their movement, to carry out the analysis of these factors on the basis of these factors to search for suspects and cars in the city.
 Video surveillance centers, in particular within one of the most important areas of public safety – traffic safety – have the ability to recognize prohibited or atypical traffic, to capture the intersection of the restricted area or the intersection of the visual line, to respond to the passage of people in a given direction, to identify traffic conditions, to detect traffic density, congestion and mass congestion of vehicles, to respond to the appearance of people in the surveillance zone, etc.
 Based on the review of the experience of introduction and use of video surveillance systems in Ukraine and analysis of their development in developed countries, the authors have suggested the ways to modernize and further develop security systems in Ukraine by using CCTV cameras.

The subject of study. A new algorithm for real-time data compression in a video surveillance system from an unmanned aerial vehicle is proposed. The algorithm makes it possible to obtain different degrees of video data compression using different codecs for specific video recording conditions (UAV altitude and flight speed, nature of the observed terrain, etc.), and also provides the ability to transmit video data to the flight control point via radio channel. A detailed analysis of the quality of the proposed algorithm for shooting in various video formats that meet international standards (HD and Full HD) was carried out, both in terms of visual perception and objective criteria. The purpose of the study was to conduct a comprehensive research on the problem of improving the quality of the video surveillance system from the UAV and to create a video compression algorithm that allows broadcasting video data to the control center in real time with high quality. Objectives. To develop and programmatically implement a video compression algorithm based on modern codecs for video surveillance sys­tems on board a UAV. To use modern video cameras and a microcomputer platform as hardware. To analyze the quality indicators of the algorithm in different conditions. Methods used. Evaluation of visual perception of video compression results, statistical analysis and processing of the parameters of the analyzed video sequence as a random spatio-temporal process. Results. A new video data compression algorithm for the UAV video surveillance system based on the Raspberry Pi 5 microcomputer using modern video compression codecs H.264 and H.265 was synthesized. The algorithm provides the required compression ratio and data transmission rate over the radio channel between the UAV and the flight control center. Detailed estimates of the quality of the algorithm are obtained. Conclusions. Scientific novelty of the results: A new universal video data compression algorithm for video surveillance systems on board UAVs with the ability to transmit video over a radio channel in real time has been proposed and implemented. The quality of the proposed algorithm is verified on real video surveillance results. The proposed algorithm is implemented in the Python software environment.

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in which the fuzzy ontology consists of fuzzy ontology structure and instances. Firstly, the fuzzy UML model is investigated in detail, and a kind of formal definition of fuzzy UML models is proposed. Then, a kind of fuzzy ontology called fuzzy OWL DL ontology is introduced. Furthermore, we consider the fuzzy UML model and the corresponding fuzzy UML instantiations (i.e., object diagrams) simultaneously, and translate them into the fuzzy ontology structure and the fuzzy ontology instances, respectively. In addition, since a fuzzy OWL DL ontology is equivalent to a fuzzy Description Logic f-SHOIN(D) knowledge base, how the reasoning problems of fuzzy UML models (e.g., consistency, subsumption, equivalence, and redundancy) may be reasoned through reasoning mechanism of f-SHOIN(D) is investigated, which can help to construct fuzzy ontologies more exactly.

Video surveillance becomes more and more popular as it is a high-tech method to protect personal safety and public property. Recently it is not the privilege of police and common people can view videos of many public places lively or by on-demand way. Traditional client-server based video streaming solutions incur expensive bandwidth provision cost on the server and are not scale well. Peer-to-Peer (P2P) streaming is a new paradigm to build distributed networking video surveillance applications. Recently, several P2P-TV systems have been deployed to provide live and on-demand video streaming services on the Internet at low server cost. But it is seldom to use p2p streaming in video surveillance. In this paper, we provide the design of large scale video surveillance system client based on p2p streaming. Given the particularity of video surveillance, such as high churn and the heterogeneity of user access network and delay-tolerant, we construct the end-hosts into mesh application layer topology and adopt pull-push mode to delivery the data. Based on test on our previous implement of e-Touch video surveillance system, it shows that it is feasibly to realize video client using p2p streaming technology and simulation results shows single streaming media server can support tens of thousands of users simultaneously participating in the system.

The existing ontology reasoners are all unilaterally description logic-based or rule-based, and their abilities to deal with fuzzy ontology are limited. It is the diversity of fuzzy ontology representation methods among them that leads to a low processing efficiency of fuzzy ontology. To solve this bottle-neck problem of fuzzy ontology-based knowledge reasoning, fuzzy ontology, fuzzy ontology representation method and fuzzy description logic are investigated, and the existing fuzzy ontology reasoners are comparatively analyzed. By virtue of the advantages of the typical reasoners such as Pellet, JenaAPI and fuzzyDL (fuzzy Description Logic), a method combining rule and description logic reasoning is designed, and a framework about fuzzy ontology based knowledge reasoning is proposed. By reducing fuzzy ontology, checking consistency and repairing inconsistency, the efficiency and accuracy of fuzzy ontology based knowledge reasoning can be improved. Finally, an experiment is given to verify the validity of proposed method.

The identification of culprits from videosurveillance systems brings about relevant problems for what concerns the reliability of comparison and quantification of the degree of match between the culprit and the suspect. The actual videosurveillance give only 2D images of the culprit, which can be compared to a 3D model of the suspect’s face in 2D-3D superimposition. However, in the next future the videosurveillance systems will provide a 3D image of the filmed persons, which may allow the operator to perform 3D-3D superimposition. This study aims at exposing a pilot study based on 10 male subjects: the face from each individual underwent two acquisitions by stereophotogrammetry. On each 3D facial model, 9 landmarks (right and left: en, ex, ch: midline: se, gn, prn) were identified by VAM® software; the acquisitions of each individual were then superimposed with the models of all the other subjects in order to reduce the distance between the corresponding points. The RMS value (Root Mean Square) between the two models was then calculated. When the two superimposed models belonged to the same individual, RMS value was in mean 1.58 mm (SD: 0.61 mm), whereas when the two models were taken from different persons, the same value was 4.54 mm (SD: 2.02 mm) with a statistically significant (p<0.0001). This pilot study shows that the judgment of identification based on 3D-3D superimposition may provide reliable results. The improvement specific methods of 3D-3D comparison may provide in the next future relevant advantages in this field of forensic anthropology.

A new framework for deep learning video based Human Action Recognition on the edge

Guangdong Power Grid Corporation has put forward and implemented based on IP network digital, Linux, industrial grade layered architecture of substation video and environment surveillance system. In order to improve video coding efficiency and meet requirements of users on HD images resolution of substation video and environment surveillance system, this paper proposes an improved implicit motion information inter prediction algorithm for P slice in H.264 based on the spatio-temporal adaptive localized learning model. This algorithm essentially takes advantage of the temporal and spatial correlations that may exist within frames at the block level, and reduces the bits required for encoding motion information while retaining or even improving encoding quality. The simulation results show that the proposed algorithm improves encoding efficiency compared with the H.264 standard with 0.10 dB (QCIF) and 0.17 dB (WQVGA) average PSNR gain, with relatively increase in complexity.

The innovative concept of Complex Fuzzy Set is introduced. The objective of the this paper to investigate the concept of Complex Fuzzy set in constraint to a traditional Fuzzy set , where the membership function ranges from [0, 1], but in the Complex fuzzy set extended to a unit circle in a complex plane, where the member ship function in the form of complex number. The Compressive study of mathematical operation of Complex Fuzzy set is presented. The basic operation like addition, subtraction, multiplication and division are described here. The Novel idea of this paper to measure the similarity between two fuzzy relations by evaluating δ -equality. Here also we introduce the probabilistic interpretation of the complex fuzzy set where we attempted to clarify the distinction between Fuzzy logic and probability. KEY WORD Complex fuzzy set, Complex fuzzy relation, complement of complex fuzzy relation, δ -equality, probability.

Video surveillance technology plays an important role in the security field, with its intuitive, convenient and abundant information and is widely used in city traffic, civil security and other various fields especially in the important safety departments or major events. In this paper, according to the video surveillance system features and functional requirements, we use embedded system ,OMAP4460 and Android operating system on the basis of the panoramic technology to realize vertical 180 degrees horizontal 360-degree panoramic video surveillance. In the paper we profoundly analyze and discuss the embedded panoramic video surveillance system mainly from the hardware platform structures, the design of the peripheral circuit and the panorama function implement. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1874

Detection and tracking of moving objects has immense relevance when it comes to tasks such as video surveillance and computer vision applications. The design of a video surveillance system is directed on automatic identification of events of interest, especially on tracking and classification of moving objects. A video surveillance system consists of three phases: moving object recognition, tracking, and decision making. This paper focuses on detection of human beings in a scene, and tracking those people as long as they stay in the scene by identifying individual persons. Automating the video surveillance process will help in effortless monitoring of the sensitive areas with less human resource utilisation.