Motion Detection in Digital Video Recording Format with Static Background

In recent studies video surveillance become an important task for identifying the motion detection (moving objects) to avoid the burglars in home security systems. Most of the video surveillance systems works on algorithms like background image subtraction, double background filter (DBF), optical flow method for motion detection where video is recorded by digital video recorder when an moving object is identified to save the memory, but in due process some frames are missed even moving objects are identified as they are treated as stable objects (without considering the minor movement in objects under threshold value), here we propose a method to integrate background image subtraction and double background filtering with morphological dilations to identify the moving objects and enhance the video quality by using the trained filters to improve the low quality frames.

Malaysia urbanization rate has been growing fast due to rapid development and modernization (Economy Planning Unit, 2015) (World Bank, 2015). With the fast growing urban population, the property crimes associated also rises at an alarming rate (United Nations Human Settlements Program, 2007) ( Mohit Mohammad Abdul, Elsawahli H. Mohamed Hassan, 2015) (Ministry of Home Affairs, 2015). Video surveillance system is one of the trusted and efficient security systems to protect people and property against criminal (Varij Ken, 2015). Three problems and issues regarding current video surveillance system are functionality, flexibility and efficiency. The aim of this project is to design and develop an embedded motion detecting video surveillance system and to produce a functioning prototype as the final result to solve these problems. In order to achieve the aim of this project, three objectives are set to be attained. These objectives are to design and develop a motion detection algorithm based on OpenCV library functions using camera as sensor, to process and execute the algorithm using an embedded micro-computer and to compare its processing performance with a PC and to enable wireless user alert using LAN and internet connection. The first objective is achieved as the motion detection algorithm designed and developed based on background subtraction method using camera sensor and openCV library function is proven to functioning well in detecting for motion changes. The algorithm performs successfully both in BeagleBone Black module and PC and is able to deliver outputs required for embedded motion detection video surveillance system. The second objective is achieved as BeagleBone Black (BBB) module, a micro-computer embedded system is used as main processor for the embedded motion-detecting video surveillance system. The BBB module is able to process and execute the motion detection algorithm designed with image processing functions from OpenCV library. The third objective is achieved as embedded motion-detecting video surveillance system is equipped with wireless user alert function using local area network and internet connection. The system is able to send real-time alert to the user via email attached with the image captured and detected as a threat by the system.

Autonomous multisensor tracking surveillance system

In recent years, the rapid expansion of multimedia applications, partly due to the exponential growth of the Internet, has proliferated over the daily life of computer users (Yang & Hurson, 2006). The integration of wireless communication, pervasive computing, and ubiquitous data processing with multimedia database systems has enabled the connection and fusion of distributed multimedia data sources. In addition, the emerging applications, such as smart classroom, digital library, habitat/environment surveillance, traffic monitoring, and battlefield sensing, have provided increasing motivation for conducting research on multimedia content representation, data delivery and dissemination, data fusion and analysis, and contentbased retrieval. Consequently, research on multimedia technologies is of increasing importance in computer society. In contrast with traditional text-based systems, multimedia applications usually incorporate much more powerful descriptions of human thought—video, audio, and images (Karpouzis, Raouzaiou, Tzouveli, Iaonnou, & Kollias, 2003; Liu, Bao, Yu, & Xu, 2005; Yang & Hurson, 2005). Moreover, the large collections of data in multimedia systems make it possible to resolve more complex data operations such as imprecise query or content-based retrieval. For instance, the image database systems may accept an example picture and return the most similar images of the example (Cox, Miller, & Minka, 2000; Hsu, Chua, & Pung, 2000; Huang, Chang, & Huang, 2003). However, the conveniences of multimedia applications come with challenges to the existing data management schemes: • Efficiency: Multimedia applications generally require more resources; however, the storage space and processing power are limited in many practical systems, for example, mobile devices and wireless networks (Yang & Hurson, 2005). Due to the large data volume and complicated operations of multimedia applications, new methods are needed to facilitate efficient representation, retrieval, and processing of multimedia data while considering the technical constraints. • Semantic Gap: There is a gap between user perception of multimedia entities and physical representation/access mechanism of multimedia data. Users often browse and desire to access multimedia data at the object level (“entities” such as human beings, animals, or buildings). However, the existing multimedia retrieval systems tend to access multimedia data based on their lower-level features (“characteristics” such as color patterns and textures), with little regard to combining these features into data objects. This representation gap often leads to higher processing cost and unexpected retrieval results. The representation of multimedia data according to human’s perspective is one of the focuses in recent research activities; however, few existing systems provide automated identification or classification of objects from general multimedia collections. • Heterogeneity: The collections of multimedia data are often diverse and poorly indexed. In a distributed environment, because of the autonomy and heterogeneity of data sources, multimedia data objects are often represented in heterogeneous formats. The difference in data formats further leads to the difficulty of incorporating multimedia data objects under a unique indexing framework. • Semantic Unawareness: The present research on content-based multimedia retrieval is based on feature vectors—features are extracted from audio/video streams or image pixels, empirically or heuristically, and combined into vectors according to the application criteria. Because of the application-specific multimedia formats, the feature-based paradigm lacks scalability and accuracy.

In the efforts to fight crimes and terrorism, governments install video surveillance equipment that works like thousands of eyes in airports, ports, major roadways, government buildings, and important facilities. However, such kinds of surveillance system were hardware dependence and high budget for the installation. This paper is addressed to the development of the web-based real-time video surveillance framework. The proposed real-time many-to-many surveillance system can provide users to dynamically plug in their digital photographic equipments, such as web camera and CCTV (digital video) through the Internet without the installation of extra software. The constructed system also achieves an adjustable motion detection facility which can trigger the video recording and the real-time e-mail notification via PC or 3G mobile message.

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.

This paper investigates a habituation based approach in detecting visual changes using video surveillance systems in a passive environment. Various techniques have been introduced for dynamic environment such as motion detection, object classification and behaviour analysis. However, in a passive environment, most of the scenes recorded by the surveillance system are normal. Therefore, implementing a complex analysis all the time in the passive environment resulting on computationally expensive, especially when using a high video resolution. Thus, a mechanism of attention is required, where the system only responds to an abnormal event. This paper proposed a novelty detection mechanism in detecting visual changes and a habituation based approach to measure the level of novelty. The objective of the paper is to investigate the feasibility of the habituation based approach in detecting visual changes. Experiment results show that the approach are able to accurately detect the presence of novelty as deviations from the learned knowledge.

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.

To understand how people really interact with digital TV and digital video recorder (DVR) systems, 10 people were recruited, 6 with DVRs and 4 with non-DVR digital cable or satellite TV systems. Video-recording equipment was set up in their homes to record their TV viewing. The person viewing as well as the TV content being viewed were recorded as the person watched TV. Afterwards, the recordings were reviewed and analyzed by the experimenter together with the viewer, who then described his or her behavior. The results show that overall the most common way people interacted with TV was in avoiding advertisements and finding new programming to watch when the current show ended. These results can be used to find ways to improve how users experience TV systems.

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.&#x0D; 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.&#x0D; 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.&#x0D; 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 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.

Network video surveillance has been a popular security application for many years. Target tracking in a cluttered environment remains one of the challenging problems of video surveillance. The task of target tracking is a key component of video surveillance and monitoring systems. It provides input to high-level processing such as recognition, access control or re-identification or is used to initialize the analysis and classification of human activities. Intelligent and automated security surveillance systems have become an active research area in recent time due to an increasing demand for such systems in public areas such as airports, underground stations and mass events. In this context, tracking of stationary foreground regions is one of the most critical requirements for surveillance systems based on the tracking of abandoned or stolen objects or parked vehicles. Object tracking based techniques are the most popular choice to detect stationary foreground objects because they work reasonably well when the camera is stationary and the change in ambient lighting is gradual, and they also represent the most popular choice to separate foreground objects from the current frame. In this paper, we did the literature survey on different technique and finally carried out our methodology for the same situation.

Now the visual surveillance system has been studied extensively, and has made great progress, but many surveillance systems are only applied in specific sites, and the level of intelligence is not high. Intelligent video surveillance system mainly carries out research on techniques of detection and tracking of moving targets, which are very important for detection and understanding of abnormal behaviors, so the effect of moving detection directly affects the overall effect of video surveillance systems, moving detection of intelligent visual surveillance is mainly target classification, tracking, detection, behavior recognition and so on. Detection and tracking of moving targets in surveillance applications has greatly improved intelligence, accuracy and reliability of video surveillance and has greatly reduced the burden on staff.

In general the motion detection method look for differences between some sequentially ordered frame followed by threshold process to detect whether occurred any movement or not. Process of deciding threshold to show any movement in dynamic environment has high enough difficulty related with many "fake" movement which appears as the result of illumination change factor, sunshine or light source, color differences as the effect of automatic color correction inside the device, shadow movement, etc. Temporal mechanism using standard deviation is a mechanism that proposed in Video Surveillance and Monitoring (VSAM) research project at Carniagie Mellon Universitiy. In this research we proposed one hybrid scheme to determine initial value of motion thresholding to create an adaptive moving detection system. We proposed the using of local standard deviation in 3 x 3 pixel area as initial value for mechanism that was defined by Robert et al. This hybrid scheme used moving standard deviation value in each pixel to create adaptive threshold value for motion/ movement detection. The experiment showed that by using local standard deviation as initial value we could achieve balance accuracy for movement and non-movement detection with accuracy propostion 80.00% for video with movement and 90.91% for video without movement.

The ’’Digital Video Recording system (DVR)” has achieved the storage capacity of 9.2GByte on 12cm optical disc with a red laser by a high numerical aperture (NA=0.85) lens and thin cover layer.