CCTV Surveillance Systems Research Articles

RGB Color Model Based the Fire Detection Algorithm in Video Sequences on Wireless Sensor Network

Since the fire causes serious disasters, fire detection has been an important study to protect human life. Based on the deficiencies of existing fire detection on real-time and monitoring accuracy, the wireless sensor network technique for fire detection was introduced and needed. In this paper, we proposed the fire detection algorithm in video sequences on wireless sensor network. The proposed fire detection algorithm processes visual information acquired through static camera that lets us incorporate the algorithm to CCTV surveillance system, and therefore does not require an additional expanses on conventional fire sensors. Fire detection method based on motion information, any input image, is compared to background in order to identify foreground. Background is statistically modeled by mixture of Gaussians. To detect the foreground of video sequences, the proposed color detection algorithm was performed in RGB space. The procedure of algorithm eliminates all objects that do not fulfill color requirements without fire-like objects. And the change map and blob's area are computed, change map shows temporal variation of pixel between two consecutive binary frames, and percentage area increase or decrease characterizes a fire property for swinging.

A CCTV Lens Robot with Four DOFs for Object Searching

Nowadays CCTV surveillance system can be seen everywhere in our cities[. The limited monitoring range is the main problem for its application. Although some of the monitoring unit bracket allows the camera to search within a certain range, its huge size severely limits its use in some conditions. For example, it is not appropriate to apply in a small space[. Considering this, we design a small space rotating device which can make the camera rotate around the X and Y about 40 degrees respectively. Experiments show that the CCTV rotation bracket has its features such as compact, light weighted structure and easy to control. It is suitable for application on the small size device.

INIX Technologies Holdings Berhad

INIX Technologies Holdings Berhad (hereinafter referred to ‘the Company’ or ‘INIX’) was incorporated on 13 September 2004 in Malaysia under the Companies Act, 1965 as a public company. It was established to be an investment holding company in conjunction with the listing of the group on the MESDAQ Market. INIX is principally involved in the development and sales of integrated intelligent wireless security, automation and CCTV surveillance systems designed specifically for home and business installation. The group was formed as a result of a spin off from PC Sentry Sdn Bhd in 2003. PC Sentry was established in 1995 to embark on R&D for integrated intelligent security system as well as to provide PC and system integration solutions. INIX currently sells its products under its 'Secure Smith' brand name either as off-the-shelf products or customized according to client's specifications.

Semi-automated CCTV surveillance: The effects of system confidence, system accuracy and task complexity on operator vigilance, reliance and workload

Recent advances in computer vision technology have lead to the development of various automatic surveillance systems, however their effectiveness is adversely affected by many factors and they are not completely reliable. This study investigated the potential of a semi-automated surveillance system to reduce CCTV operator workload in both detection and tracking activities. A further focus of interest was the degree of user reliance on the automated system. A simulated prototype was developed which mimicked an automated system that provided different levels of system confidence information. Dependent variable measures were taken for secondary task performance, reliance and subjective workload. When the automatic component of a semi-automatic CCTV surveillance system provided reliable system confidence information to operators, workload significantly decreased and spare mental capacity significantly increased. Providing feedback about system confidence and accuracy appears to be one important way of making the status of the automated component of the surveillance system more ‘visible’ to users and hence more effective to use.

Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework

Although automatic faces recognition has shown success for high-quality images under controlled conditions, for videobased recognition it is hard to attain similar levels of performance. We describe in this paper recent advances in a project being undertaken to trial and develop advanced surveillance systems for public safety. In this paper, we propose a local facial feature based framework for both still image and video-based face recognition. The evaluation is performed on a still image dataset LFW and a video sequence dataset MOBIO to compare 4 methods for operation on feature: feature averaging (Avg-Feature), Mutual Subspace Method (MSM), Manifold to Manifold Distance (MMS), and Affine Hull Method (AHM), and 4 methods for operation on distance on 3 different features. The experimental results show thatMulti-region Histogram (MRH) feature ismore discriminative for face recognition compared to Local Binary Patterns (LBP) and raw pixel intensity. Under the limitation on a small number of images available per person, feature averaging ismore reliable than MSM, MMD, and AHM and ismuch faster. Thus, our proposed framework--veraging MRH feature is more suitable for CCTV surveillance systems with constraints on the number of images and the speed of processing.

Automatic Motion Learning in the Presence of Anomalies Using Coefficient Feature Space Representation of Trajectories

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering of object trajectory-based video motion clips using basis function approximations. Motion cues can be extracted using a tracking algorithm on video streams from video cameras. In the proposed system, trajectories are treated as time series and modelled using orthogonal basis function representation. Various function approximations have been compared including least squares polynomial, Chebyshev polynomials, piecewise aggregate approximation, discrete Fourier transform (DFT), and modified DFT (DFT-MOD). A novel framework, namely iterative hierarchical semi-agglomerative clustering using learning vector quantization (Iterative HSACT-LVQ), is proposed for learning of patterns in the presence of significant number of anomalies in training data. In this context, anomalies are defined as atypical behavior patterns that are not represented by sufficient samples in training data and are infrequently occurring or unusual. The proposed algorithm does not require any prior knowledge about the number of patterns hidden in unclassified dataset. Experiments using complex real-life trajectory datasets demonstrate the superiority of our proposed Iterative HSACT-LVQ-based motion learning technique compared to other recent approaches.

Characteristics of abnormal large waves measured from coastal videos

Abstract. Recently, tsunami-like sea-level oscillations occurred in a region of the west coast of Korea (i.e. in the eastern part of the Yellow Sea), during a typical rough spring weather episode on 4 May 2008. The analysis of these tsunami-like abnormal waves focuses solely on the videos recorded by a CCTV surveillance system in the directions of the entrance and inside parts of a local coastal pocket beach. Time-series of the vertical and horizontal sea surface oscillations were extracted from the video recordings through calibrating image distortions, accumulating 1-D intensity arrays along the line transects of interest in time, and identifying the trajectories of the oscillations. Frequency and time-domain analysis of the time-series signals revealed that the maximum height of the tsunami-like waves reached 1.3 m, having a dominant period of 185 s (3.1 min). In addition, the results indicate that the celerity of the maximum height wave approximated 7.3 m/s, which lead to the losses of life of several people who could not escape immediately from the fast tsunami flooding the shore.

Motion-based behaviour learning, profiling and classification in the presence of anomalies

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. Motion trajectories provide rich spatiotemporal information about an object's activity. This paper presents a novel technique for clustering and classification of motion. In the proposed motion learning system, trajectories are treated as time series and modelled using modified DFT (discrete fourier transform)-based coefficient feature space representation. A framework (iterative HSACT-LVQ (hierarchical semi-agglomerative clustering-learning vector quantization)) is proposed for learning of patterns in the presence of significant number of anomalies in training data. A novel modelling technique, referred to as m-Mediods, is also proposed that models the class containing n members with m Mediods. Once the m-Mediods-based model for all the classes have been learnt, the classification of new trajectories and anomaly detection can be performed by checking the closeness of said trajectory to the models of known classes. A mechanism based on agglomerative approach is proposed for anomaly detection. Our proposed techniques are validated using variety of simulated and complex real life trajectory data sets.

Intelligent CCTV for Mass Transport Security: Challenges and Opportunities for Video and Face Processing

CCTV surveillance systems have long been promoted as being effective in improving public safety. However due to the amount of cameras installed, many sites have abandoned expensive human monitoring and only record video for forensic purposes. One of the sought-after capabilities of an automated surveillance system is “face in the crowd” recognition, in public spaces such as mass transit centres. Apart from accuracy and robustness to nuisance factors such as pose variations, in such surveillance situations the other important factors are scalability and fast performance. We evaluate recent approaches to the recognition of faces at large pose angles from a gallery of frontal images and propose novel adaptations as well as modifications. We compare and contrast the accuracy, robustness and speed of an Active Appearance Model (AAM) based method (where realistic frontal faces are synthesized from non-frontal probe faces) against bag-of-features methods. We show a novel approach where the performance of the AAM based technique is increased by side-stepping the image synthesis step, also resulting in a considerable speedup. Additionally, we adapt a histogram-based bag-of-features technique to face classification and contrast its properties to a previously proposed direct bag-of-features method. We further show that the two bag-of-features approaches can be considerably sped up, without a loss in classification accuracy, via an approximation of the exponential function. Experiments on the FERET and PIE databases suggest that the bag-of-features techniques generally attain better performance, with significantly lower computational loads. The histogrambased bag-of-features technique is capable of achieving an average recognition accuracy of 89% for pose angles of around 25 degrees. Finally, we provide a discussion on implementation as well as legal challenges surrounding research on automated surveillance.

The thinking eye

PurposeTo discuss and review the shift to computer enhanced self‐monitoring CCTV surveillance systems of public spaces and the social implications of this shift.Design/methodology/approachA review of the research and evaluation literature concerning CCTV surveillance systems culling out the history of public space CCTV systems and the concerns associated with first and second generation CCTV surveillance.FindingsThe main difference between first and second generation surveillance is the change from a “dumb camera” that needs a human eye to evaluate its images to a computer‐linked camera system that evaluates its own video images. Second generation systems reduce the human factor in surveillance and address some of the basic concerns associated with first generation surveillance systems such as data swamping, boredom, voyeurism, and profiling. Their enhanced capabilities, though, raise new concerns, particularly the expansion of surveillance and its intrusiveness.Research limitations/implicationsAdditional research is needed to assess CCTV surveillance on a set of social dynamics such as informal guardianship activities by citizens.Practical implicationsThe adoption of computer‐enhanced CCTV surveillance systems should not be an automatic response to a public space security problem and their deployment should not be decided simply on the technology's availability or cost.Originality/valueThis paper provides a concise overview of the concerns associated with first generation CCTV surveillance and how the evolution of computer‐enhanced CCTV surveillance systems will alter and add to these concerns. For researchers it details research questions that need to be addressed. For practitioners and government officials considering the use of public space CCTV surveillance it provides a set of issues that should be considered prior to system adoption or deployment.

Advanced Surveillance: From Tracking To Event Detection

The authors suggest an automatic surveillance system based on a robust and simple algorithm for foreground detection and tracking of multiple objects in complex environments. It is possible to obtain enough information from the characteristics of the detected objects, their centroid's position and their trajectories as to detect some predefined events. Working without the operator supervision the application analyses the video streams from the CCTV surveillance system, discarding empty scenes for monitoring or recording, showing to the operator those fulfilling predefined requirements, like crowding, and rising an alarm to call the operator attention when it decides that an event may be taking place. The application is based on very simple algorithms in order to allow a single computer the analysis of up to eight video streams in real-time. Blob and scene characteristics are used to create a low-level description of predefined events. The application detects events by comparing the sequence's parameters with those associated with the events for the current scenario. The provided examples are tailored to the specific demands of a surveillance system in a public transport environment.

Automatic CCTV surveillance-towards the VIRTUAL GUARD

The VIRTUAL GUARD is a general-purpose computer-based CCTV surveillance system for detecting potential criminal activity in public areas. The system monitors all activity in the surveillance area, the vast majority of which is people innocently going about their normal business. It will alarm when the observed activities of particular pedestrians and vehicles match any of the pre-defined "suspicious behaviour criteria" programmed into the system. At the same time as analysing movement behaviour, the system uses computer-controlled pan tilt zoom cameras to obtain close-up video recordings of any pedestrians and vehicles at the scene. The system can provide automatic surveillance in many different situations, from parking areas and commercial districts, to housing, recreational and transport facilities. It is particularly suited to the protection of government or commercial buildings located on city streets or other public areas where it is not possible to install perimeter fences.

Exploring the Role of CCTV Surveillance Systems in Town Centre Management

Within the UK the past three decades have witnessed dramatic and continuing changes in the geography of retail provision. During this period the traditional supremacy of town and city centres at the top of the retail hierarchy has been increasingly successfully challenged by the development and diversification of out‐of‐town and edge of town shopping facilities. This ‘out of town exodus’ (Schiller, 1987) can be traced from the food superstores opened by grocery retailers from the late 1960's onwards through the development of retail warehouses, retail parks and regional shopping centres (Guy, 1994) to a more recent ‘fourth wave’ (Fernie, 1995) which include warehouse clubs, factory outlet centres and airport retailing. The cumulative effects of these developments are seen to pose a major challenge to retail businesses in town and city centres and perhaps more fundamentally to the centres themselves. The traditional spirit of the UK's town and country planning policies, first established some fifty years ago, was to positively support retail activity in town and city centres and to restrict out of town retail development (Guy, 1994). However, from the early 1980's onwards, such policies had only a limited effect in stemming the tide of retail decentralisation and they often seemed to be honoured more in the breach than in the observance.

VIDEQUAL. A tool for quantitative measurement of fixed CCTV surveillance systems

A range of specified measurements is now being regularly used as part of the specification and commissioning requirements for CCTV systems, As a result of this increased activity during commissioning it has been noted that the time and effort required is extensive. Also there are still a number of subjective observations which must be made, To build on this work it is proposed to develop an automated measurement system which will utilise measured video signal parameters as a means of defining system performance.