Motion Detectors

The first five sections represent the foundation and offer various intelligent algorithms that are the basics for motion detectors and their realization. There are two classes of security system alarm triggers: physical motion sensor and visual motion sensors. Both analog motion detectors and digital motion detectors belong to the group of visual motion sensors. Digital motion detector systems should differentiate between activities that are acceptable and those that breach security. When security-breaching acts occur, the system should identify the individuals and instruct security personnel what to do. Motion detectors can surveil, detect, and assess, as well as analyze information and distribute information to security personnel. Motion detector systems drastically reduce the load of footage that guards must watch for a long period of time. Automated motion detectors are now a standard for serious medium to large security installations; they are necessary for high detection capabilities. All security systems must have an alarming device to signal the guard of irregular motion in a scene, even systems that have a tiny or huge number of cameras.

Cameras

The very first element in the video surveillance system is the device that captures the images, which is the camera. This is a very important chapter in the book as it discusses the concepts of analog and digital cameras, its various designs, and camera specifications. Proper camera choice as well as setting is a very important issue in video surveillance system installation and design.

High Definition Television (HDTV) and Video Surveillance

High definition television is becoming ever more popular, opening up the market to new high-definition technologies. Image quality and color fidelity have experienced improvements faster than ever. The video surveillance market has been affected by high definition television demand. Since video surveillance calls for large amounts of image data, high-quality video frame rates are generally compromised. However, a network camera that conforms to high definition television standards shows good performance in high frame rate, resolution, and color fidelity. High quality network cameras are a good choice for surveillance video quality.

Automatic Object Classification and Image Retrieval by Sobel Edge Detection

A comparative study of ability of the proposed novel image retrieval algorithms is performed to provide automated object classification invariant of rotation, translation, and scaling. Simple cosine similarity coefficient methods are analyzed. Considering applied cosine similarity coefficient methods, the two following approaches were tested and compared: the processing of the whole image and the processing of the image that contains edges extracted by the application of the Sobel edge detector. Numerical experiments on a real database sets indicate feasibility of the presented approach as an automated object classification tool without special image pre-processing.

Algorithm for Petro-Graphic Color Image Segmentation Used in Oil Exploration

A new heuristic algorithm for porosity segmentation for the colored petro-graphic images is proposed. The proposed algorithm automatically detects the porosities that represent the presence of oil, gas, or even water in the analyzed thin section rock segment based on the colour of the porosity area filled with dies in the analyzed sample. For the purpose of the oil exploration, the thin section fragments are died in order to emphasize the porosities that are analyzed under the microscope. The percentage of the porosity is directly proportional to the probability of the oil, gas, or even water presence in the area where the drilling is performed (i.e. the increased porosity indicates the higher probability of oil existence in the region). The proposed automatic algorithm shows better results than the existing K-means segmentation method.

Algorithm for Automatic Pattern Classification Designed for Real Metallographic Images

The problem of automatic pattern classification in real metallographic images from the steel plant ArcelorMittal Ostrava is addressed. The goal is to monitor the process quality in the steel plant. In the images of metal, there are dark dots that are produced by imperfections along the central axis of each plate. It is necessary to determine automatically the number and sizes of these dots. The number and sizes of the dots is a measure of how imperfect each plate is. The process is presented that segments the area of plates that contains segregation, identifies those rows of pixels along which the dots lie, and counts the pixels that are marked as dots by evaluating all the vertical columns of pixels that intersect the rows that contain the dots. The threshold value is set to be 95% of the mean value of grey scale for each column of pixels and makes the dots white. White dots that are most likely noise are removed to identify dots that are smaller than 4 connected pixels across. The explanations related to the obtained results are firmly related to the information provided by human experts.

Improved Illumination Independent Moving Object Detection Algorithm

A real time change detection technique is proposed in order to detect the moving objects in a real image sequence. The described method is independent of the illumination of the analyzed scene. It is based on a comparison of corresponding pixels that belong to different frames and combines time and space analysis, which augments the algorithm’s precision and accuracy. The efficiency of the described technique is illustrated on a real world interior video sequence recorded under significant illumination changes.

Video Surveillance System Applications

The fact that video surveillance is such an effective system especially when one thinks of its widespread use attests to its low investment cost. This chapter contains information about design guidelines, hardware information, specific examples, and necessary parameters to be addressed while designing representative security video surveillance system applications: protection of all assets and personnel, calculation of the overall cost of the video system, surveillance target (assets and/or personnel), surveillance timing schedule, type and number of cameras needed, camera placement, field of view required, console room monitoring equipment, number and types of monitors, number of displays per monitor, number and type of recorders, digital recording technology needed, type of video switchers, type of video printer, if additional lighting is required, if intensified or thermal IR cameras are required, if sensors at doors, windows, and perimeters that are integrated with video signals are needed, digital video motion detectors placement, IP cameras, type of signal and video transmission, type of digital transmission, type of 802.11 protocol, type of compression (MPEG-4 or H.264), and the necessity of encryption or scrambling.

Algorithm for Automated Iris Recognition Using Biorthogonal Wavelets

A comparative study of ability of two novel image retrieval algorithms to provide automated touch-free identification of persons by iris recognition is presented. Namely, applied biorthogonal wavelet methods and the SVD-Free Latent Semantic method are analyzed. Moreover, in case of the applied biorthogonal wavelet method, two approaches were tested and compared: the processing of the whole image and the processing of the image converted to a vector. The point is that different methods for getting rid of the noise were successfully applied in both cases. Numerical experiments on a real biometric database indicate feasibility of the presented approach as an automated iris recognition tool without special image pre-processing.

IP Video Surveillance System

The chapter is a summary of IP surveillance systems: basic functions, the advantages of network video, customizing surveillance applications, and possible legal concerns. The most important step one can take before installing IP surveillance system is to define goals and requirements. Once these are determined, the video system can be set up. The required goals to be determined are the following: definition of the video surveillance system needs (installation plan, area of coverage, camera positioning, illumination conditions determination, camera cabling, the recording server positioning), network camera and/or video encoder selection (image quality, lens selection, network camera selection, Power over Ethernet [PoE], video motion detection, audio, accessories selection, testing), hardware (switches, additional light sources, power supplies, additional server for video management software, hard drives), software (software package selection, licenses, image quality and frame rate requirements, IP address range calculation, hard disk usage calculation, camera configuration, video motion detection settings, user access definition), and maintenance.