Legal aspects of data mining algorithms for stream processing in traffic surveillance

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

As society develops, the demand for security is rapidly increasing. Accordingly, there is growing interest in research on methods to detect and prevent abnormal behavior using surveillance cameras in public places and private spaces such as shopping malls and airports for human safety. Many detection techniques based on deep learning models have been researched in the field of abnormal behavior detection. However, due to the lack of labeled abnormal behavior data, there are significant difficulties in developing an effective detection system. This paper surveys methods for deep learning methods to detect abnormal human behavior in surveillance video and presents recent techniques. First, I will introduce popular datasets that have often been used in previous research. After that, we categorized the existing methods for detecting abnormal behavior using deep learning into three types: supervised learning, unsupervised learning, and partially-supervised learning. We then explained the basic concepts and advantages of each method and summarized their shortcomings. We also briefly describe future research directions based on the advantages and disadvantages of each method. Based on this, it is expected that the technology of video surveillance systems that apply abnormal behavior detection will further develop.

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.

In recent times, cloud computing has become the widely accepted technology for moving the database over the cloud, which has brought revolution in the IT industry, this term is recently coined as Database as a Service (DBaaS). Mostly cloud databases are used for data intensive applications such as data warehousing, data mining and monitoring purpose. On the advent of smart cities concept, massively scalable video surveillance has become the necessity in public area. In this paper, we proposed a database as a service to support video surveillance system on the cloud environment with real time video analytics. In this regard, we have explored Trove (Open stack cloud DBaaS component) to facilitate scalability and availability for the video surveillance system furthermore we have also proposed data processing architecture based on open CV, Apache Kafka and Apache spark.

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.

Video surveillance systems have become an indispensable tool for the security and organization of public and private areas. Most of the current commercial video surveillance systems rely on a classical client/server architecture to perform person and object recognition. In order to support the more complex and advanced video surveillance systems proposed in the last years, companies are required to invest resources in order to maintain the servers dedicated to the recognition tasks. In this work we propose a novel distributed protocol that exploits the computational capabilities of the surveillance devices (i.e. cameras) to perform the recognition of the person. The cameras fall back to a centralized server if their hardware capabilities are not enough to perform the recognition. By means of simulations, we show that our algorithm is able to reduce up to 50% the load of the server with no negative impact on the quality of the surveillance service.

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

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 new approach to obtain metric data from video surveillance: Preliminary evaluation of a low-cost stereo-photogrammetric system

This paper proposes human tracking and recognition method in a camera network. Human matching in a multi-camera surveillance system is a fundamental issue for increasing the accuracy of recognition in multiple views of cameras. In camera network, videos have different characteristics such as pose, scale and illumination. Therefore it is necessary to use a hybrid scheme of scale invariant feature transform to detection and recognition human's behaviors. The main focus of this paper is to analyze activities for tracking and recognition humans to extract trajectories. Extracting the trajectories help to detect abnormal behavior which may be occluded in single- camera surveillance. KEYWORDS: Camera network, Multi-camera surveillance, Human's behavior, Trajectories extraction. I. INTRODUCTION Tracking and behavior recognition are two fundamental tasks in video surveillance systems which are widely employed in commercial applications for purposes of statistics gathering and processing. The number of cameras and complexity of surveillance systems have been continuously increasing to have better coverage and accuracy. Multi-camera systems become increasingly attractive in machine vision. Applications include multi view object tracking, event detection, occlusion handling and etc. In this paper, we develop method for tracking and recognition by a traffic video surveillance system of two cameras with a partially overlapping field of view. This paper is organized as follows: an overview of the past works in section2. Our proposed architecture and algorithm is presented in section3. Results of subjective evaluations and objective performance measurements with respect to Ground-truth are presented in section4. Section5 contains the conclusion.

Road segmentation plays an important role in many computer vision applications, either for in-vehicle perception or traffic surveillance. In camera-equipped vehicles, road detection methods are being developed for advanced driver assistance, lane departure, and aerial incident detection, just to cite a few. In traffic surveillance, segmenting road information brings special benefits: to automatically wrap regions of traffic analysis (consequently, speeding up flow analysis in videos), to help with the detection of driving violations (to improve contextual information in videos of traffic), and so forth. Methods and techniques can be used interchangeably for both types of application. Particularly, we are interested in segmenting road regions from the remaining of an image, aiming to support traffic flow analysis tasks. In our proposed method, road segmentation relies on a superpixel detection based on a novel edge density estimation method; in each superpixel, priors are extracted from features of gray-amount, texture homogeneity, traffic motion and horizon line. A feature vector with all those priors feeds a support vector machine classifier, which ultimately takes the superpixel-wise decision of being a road or not. A dataset of challenging scenes was gathered from traffic video surveillance cameras, in our city, to demonstrate the effectiveness of the 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.

Nowadays, the human behavior analysis by computer vision techniques has been an interesting issue for researchers. Automatic recognition of actions in video allows automation of many otherwise manually intensive tasks such as video surveillance. Video surveillance system especially for elderly care and their behavior analysis has an important role to take care of aged, impatient or bedridden persons. In this paper, we propose a high accuracy human action classification and recognition method using hidden Markov model classifier. In our approach, first, we use star skeleton feature extraction method to extract extremities of human body silhouette to produce feature vectors as inputs of hidden Markov model classifier. Then, hidden Markov model, which is learned and used in our proposed surveillance system, classifies the investigated behaviors and detects abnormal actions with high accuracy in comparison by other abnormal detection reported in previous works. The accuracy about 94% resulted from confusion matrix approve the efficiency of the proposed method when compared with its counterparts for abnormal action detection.

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.

Nowadays, video surveillance systems are part of our daily life, because of their role in ensuring the security of goods and people this generates a huge amount of video data. Thus, several research works based on the ontology paradigm have tried to develop an efficient system to index and search precisely a very large volume of videos. Due to their semantic expressiveness, ontologies are undoubtedly very much in demand in recent years in the field of video surveillance to overcome the problem of the semantic gap between the interpretation of the data extracted from the low level and the high-level semantics of the video. Despite its good expressiveness of semantics, a classical ontology may not be sufficient for good handling of uncertainty, which is however commonly present in the video surveillance domain, hence the need to consider a new ontological approach that will better represent uncertainty. Fuzzy logic is recognized as a powerful tool for dealing with vague, incomplete, imperfect, or uncertain data or information. In this work, we develop a new ontological approach based on fuzzy logic. All the relevant fuzzy concepts such as Video_Objects, Video_Events, Video_Sequences, that could appear in a video surveillance domain are well represented with their fuzzy Ontology DataProperty and the fuzzy relations between them (Ontology ObjectProperty). To achieve this goal, the new fuzzy video surveillance ontology is implemented using the fuzzy ontology web language 2 (fuzzy owl2) which is an extension of the standard semantic web language, ontology web language 2 (owl2).