Real-time Video Surveillance Research Articles

Implementing and testing a fiber-optic polarization-based intrusion detection system

We describe a layer-1-based intrusion detection system for fiber-optic–based networks. Layer-1-based intrusion detection represents a significant elevation in security as it prohibits an adversary from obtaining information in the first place (no cryptanalysis is possible). We describe the experimental setup of the intrusion detection system, which is based on monitoring the behavior of certain attributes of light both in unperturbed and perturbed optical fiber links. The system was tested with optical fiber links of various lengths and types, under different environmental conditions, and under changes in fiber geometry similar to what is experienced during tapping activity. Comparison of the results for perturbed and unperturbed links has shown that the state of polarization is more sensitive to intrusion activity than the degree of polarization or power of the received light. The testing was conducted in a simulated telecommunication network environment that included both underground and aerial links. The links were monitored for intrusion activity. Attempts to tap the link were easily detected with no apparent degradation in the visual quality of the real-time surveillance video.

A robust video watermarking technique for the tamper detection of surveillance systems

A digital watermark embeds an imperceptible signal into data such as audio, video and images, for different purposes including authentication and tamper detection. A real-time video surveillance ap...

High-Performance Video Condensation System

Video synopsis or condensation is a smart solution for fast video browsing and storage. However, most of the existing methods work offline, where two main phases are required. The first phase is to prepare tubes and background images. The second phase is to rearrange tubes and stitch them into backgrounds. However, with a long video sequence, the first phase is memory consuming for data storage, and the second phase is computationally expensive to rearrange all tubes simultaneously. To overcome these problems, we propose a high-performance video condensation system based on an online content-aware framework. The online framework transforms the optimization problem of tube rearrangement into a stepwise optimization problem. Therefore, it can condense video with much less memory and higher speed than the offline framework. With the aid of this transformation, the proposed system can process input videos and produce condensed videos simultaneously. Thus it is suitable for real-time endless surveillance videos. Meanwhile, the online mechanism allows users to directly visit the condensation video that has been generated. Moreover, the content-aware mechanism makes the proposed system able to automatically determine the duration of a condensed video. Finally, the proposed system uses Graphic Processing Unit (GPU) and multicore techniques to improve the speed. Extensive experiments that validate the high efficiency of the system are presented.

A behavior-based flame detection method for a real-time video surveillance system

Some characteristics of flames, including vague shapes and fire-like colors, are irregular, so it is difficult to use a video camera for real-time flame detection. To achieve fully automatic surveillance of fires, a real-time incipient flame detection approach that uses three stages of video processing is proposed in this study. Firstly, a thumbnail method is applied to the flame image for the various resolutions for real-time processing. A median filter and a Gaussian filter are used to remove the image noise. The flame edge information in the image is then enhanced, using a sharpening filter. Secondly, quasi-periodic behavior in flame boundaries is detected using motion detection and a background edge model, so some candidates for real flames can be selected. Thirdly, an evaluation method that uses four criteria, including compactness, fill rate, corner flicker rate and growth rate is used to identify the correct flame. The results are verified using videos of complex scenes at different resolutions. A comparison with other systems is made, to demonstrate that the proposed method is more accurate.

Robust Multi-person Tracking for Real-Time Intelligent Video Surveillance

We propose a novel multiple-object tracking algorithm for real-time intelligent video surveillance. We adopt particle filtering as our tracking framework. Background modeling and subtraction are used to generate a region of interest. A two-step pedestrian detection is employed to reduce the computation time of the algorithm, and an iterative particle repropagation method is proposed to enhance its tracking accuracy. A matching score for greedy data association is proposed to assign the detection results of the two-step pedestrian detector to trackers. Various experimental results demonstrate that the proposed algorithm tracks multiple objects accurately and precisely in real time.

The pedagogy of the observed: how does surveillance technology influence dance studio education?

A local trend in commercial dance studio education is the implementation of real-time digital video surveillance. This case study explores how digital video cameras in the dance studio environment affect asymmetrical power relationships already present in the commercial studio setting, as well as how surveillance impacts feminist pedagogical strategies. The panoptic mechanism in the commercial studio discussed in this case study consists of studio video cameras with live feed to monitors in the waiting area, the front desk area, as well as the director’s office. This research seeks to initiate a discussion regarding how such mediation may intensify the already panoptic atmosphere of a dance studio. Survey results indicate some emphasis placed by the parents on their positionality as client-consumers, as well as their desire to be involved in their children’s dance education, which results in their support of the surveillance. Qualitative research reveals how the teachers’ feminist pedagogical strategies are affected by video surveillance. This research adds a new dimension to the discussion around the panopticism of dance studios by focusing on an audience mediated though digital video cameras.

Survey on Background Modeling and Foreground Detection for Real Time Video Surveillance

In Image Processing, a very trivial task is to detect the changes in the multiple images of the same scene of a real time instant. The task is not only trivial but also very indispensable as it brings into play a great number of diversified subject area applications such as, remote sensing, surveillance, medical diagnosis and treatment, security surveillance, and underwater sensing. The main perspective of this survey is to give a nomenclature study of the general processing steps and prime decision rules used in the advanced change detection algorithms, which are employed for the real time video surveillance. The real time video surveillance models encompass the predictive and the background modelling techniques. The survey also emphasizes on the comparison of the processing speed of the various change detection algorithms applied in real time video surveillance.

움직임 감지를 사용하여 영상 해상도를 자동 제어하는 실시간 다중 카메라 영상 감시 시스템의 구현

본 논문에서는 움직임 감지를 사용하여 영상 해상도를 자동 제어하는 실시간 다중 카메라 영상 감시 시스템을 구현하였다. 평상시에는 4개 채널의 영상을 QVGA급으로 취득한 후 하나의 VGA급 영상으로 통합하여 전송한다. 움직임이 포착되는 경우에는 해당 채널의 영상을 자동으로 확대하여 VGA급으로 취득한 후 나머지 3개 채널의 영상을 QQVGA급으로 줄여서 오버레이한다. 이를 통하여 모든 채널의 영상을 놓치지 않으면서도 전송 대역폭을 늘리지 않고 움직임이 포착된 채널을 확대하여 감시할 수 있다. 0.18 um 공정에서 합성한 최대 동작 주파수는 110 MHz로서 이론상으로 4개의 HD급 카메라를 지원할 수 있다. This paper proposes a real-time multi-camera video surveillance system with automatic resolution control using motion detection. In ordinary times, it acquires 4 channels of QVGA images, and it merges them into single VGA image and transmit it. When motion is detected, it automatically increases the resolution of motion-occurring channel to VGA and decreases those of 3 other channels to QQVGA, and then these images are overlaid and transmitted. Thus, it can magnifies and watches the motion-occurring channel while maintaining transmission bandwidth and monitoring all other channels. When it is synthesized with 0.18 um technology, the maximum operating frequency is 110 MHz, which can theoretically support 4 HD cameras.

Multiple human detection and tracking based on head detection for real-time video surveillance

Multiple human detection and tracking is a very important and active research topic in computer vision. At present, the recognition performance is not satisfactory, which is mainly due to the fact ...

IP 카메라를 위한 HTML5 기반 통합형 Live Streaming 구현

This paper presents a unified live-streaming method based on Hypertext Mark-up Language 5(HTML5) for an IP camera which is independent of browsers of clients and is implemented with open-source libraries. Currently, conventional security systems based on analog CCTV cameras are being modified to newer surveillance systems utilizing IP cameras. These cameras offer remote surveillance and monitoring regardless of the device being used at any time, from any location. However, this approach needs live-streaming protocols to be implemented in order to verify real-time video streams and surveillance is possible after installation of separate plug-ins or special software. Recently, live streaming is being conducted through HTML5 using two different standard protocols: HLS and DASH, that works with Apple and Android products respectively. This paper proposes a live-streaming approach that is linked on either of the two protocols which makes the system independent with the browser or OS. The client is possible to monitor real-time video streams without the need of any additional plug-ins. Moreover, by implementing open source libraries, development costs and time were economized. We verified usefulness of the proposed approach through mobile devices and extendability to other various applications of the system.

Optimizing the hierarchical prediction and coding in HEVC for surveillance and conference videos with background modeling.

For the real-time and low-delay video surveillance and teleconferencing applications, the newly video coding standard HEVC can achieve much higher coding efficiency over H.264/AVC. However, we still argue that the hierarchical prediction structure in the HEVC low-delay encoder still does not fully utilize the special characteristics of surveillance and conference videos that are usually captured by stationary cameras. In this case, the background picture (G-picture), which is modeled from the original input frames, can be used to further improve the HEVC low-delay coding efficiency meanwhile reducing the complexity. Therefore, we propose an optimization method for the hierarchical prediction and coding in HEVC for these videos with background modeling. First, several experimental and theoretical analyses are conducted on how to utilize the G-picture to optimize the hierarchical prediction structure and hierarchical quantization. Following these results, we propose to encode the G-picture as the long-term reference frame to improve the background prediction, and then present a G-picture-based bit-allocation algorithm to increase the coding efficiency. Meanwhile, according to the proportions of background and foreground pixels in coding units (CUs), an adaptive speed-up algorithm is developed to classify each CU into different categories and then adopt different speed-up strategies to reduce the encoding complexity. To evaluate the performance, extensive experiments are performed on the HEVC test model. Results show our method can averagely save 39.09% bits and reduce the encoding complexity by 43.63% on surveillance videos, whereas those are 5.27% and 43.68% on conference videos.

Realization and Design of Mobile Video Surveillance Client Platform Based on Smart Phone

To realize video surveillance at any time and any place, a kind of video surveillance platform based on 3G smart phone was designed. In this paper, the general design scheme of video surveillance system was firstly introduced. Then, the surveillance software development method of smart phone client, the transmission mode and transmission protocol of surveillance video were also emphatically described. This system used SVAC which is kind of specifying surveillance video to meet security-monitoring demand. It used multi-image display to realized multi-channel video surveillance, and also used multi-thread to increase the running speed of software. Experimental results demonstrate that it can obtain stable real-time surveillance video.

Video traffic analysis using scene and vehicle models

This paper presents a complete system for analyzing a vehicle׳s behavior in the context of real-time traffic video surveillance applications. To obtain the best possible results, it is fundamental to exploit the scene characteristics and the predefined traffic rules. For that purpose, an initial training step is performed that involves estimating the geometrical structure of the road, i.e., the depth relative to the camera, the vanishing points, the road areas itself, the road decomposition (into normal and forbidden traffic lanes or areas), the traffic rules, the typical vehicle trajectories and speeds, and the lane-changing rules. This process leads to a scene model, which is used together with a simple vehicle geometrical model during the vehicle detection, tracking and trajectory estimation phases to improve the robustness against the perspective and occlusion effects. Shadow effects are also accounted for during the moving object detection phase. Finally, this spatio-temporal analysis is used to obtain information that concerns the vehicles׳ behaviors. Experiments show that the information obtained is reliable and can be computed in real time.

Video object matching across multiple non-overlapping camera views based on multi-feature fusion and incremental learning

Matching objects across multiple cameras with non-overlapping views is a necessary but difficult task in the wide area video surveillance. Owing to the lack of spatio-temporal information, only the visual information can be used in some scenarios, especially when the cameras are widely separated. This paper proposes a novel framework based on multi-feature fusion and incremental learning to match the objects across disjoint views in the absence of space–time cues. We first develop a competitive major feature histogram fusion representation (CMFH11CMFH is the abbreviation of Competitive Major Feature Histogram fusion representation.) to formulate the appearance model for characterizing the potentially matching objects. The appearances of the objects can change over time and hence the models should be continuously updated. We then adopt an improved incremental general multicategory support vector machine algorithm (IGMSVM22IGMSVM is the abbreviation of Incremental General Multicategory Support Vector Machine learning algorithm.) to update the appearance models online and match the objects based on a classification method. Only a small amount of samples are needed for building an accurate classification model in our method. Several tests are performed on CAVIAR, ISCAPS and VIPeR databases where the objects change significantly due to variations in the viewpoint, illumination and poses. Experimental results demonstrate the advantages of the proposed methodology in terms of computational efficiency, computation storage, and matching accuracy over that of other state-of-the-art classification-based matching approaches. The system developed in this research can be used in real-time video surveillance applications.

A Design and Implementation of Video Monitoring System at Smart Home

The traditional real-time video surveillance system at smart home tends to occupy a lot of resource .In order to solve this kind of problem, a design of an infrared sensor to trigger video monitoring system is proposed in this paper .The system uses arm9-Linux as the platform ,and infrared sensor as the trigger device and uses the mpeg-4 algorithm to encode the video stream finally .The article mainly introduces how to build the hardware and software platform and tests the feasibility of the system.

Adaptive Transmission Control Based on QoE Measurement of Invalid Data Rate

In the real-time network video surveillance system which uses UDP for data transmission, the packet loss and delay of H.264 video data unit would cause image pause and blur. The paused and blurry image affects the quality of users’ experience. In this paper, we put forward Idr (invalid data rate) to integrate the delay and packet loss rate and chooses code rate as the target of adjustment. We propose an adaptive transmission approach based on QoE Measurement of invalid data rate. Experiment result shows that, this approach can improve the video fluency and clarity as well as user experience.

Multipath Video Real-Time Streaming by Field-Based Anycast Routing

Wireless mesh networking (WMN) for video surveillance provides a strong potential for rapid deployment in a large community, for which reliability and survivability for real-time streaming are the key performance measure. However, current routing protocols do not provide a robust solution to meet video transmission requirements such as providing load balancing for the high data rate and delay requirements. We propose an application of field-based anycast routing (FAR) protocol, which utilizes rapid routing dynamics inspired by an electrostatic potential field model governed by Poisson's equation. This routing metric takes into account geometric proximity and congestion degree in order to increase delivery ratio and decrease end-to-end delay, which determine the quality of the delivered video. In addition, FAR protects node failure with an on-the-fly rerouting process that guarantees the continuity of video streaming. Simulation results show 100% delivery ratio in congestion situations and it shows tolerance to different delay requirements compared with AODV and FDMR protocols in terms of delivering real-time and non real-time video surveillance which verifies FAR as a strong candidate for video transmission over WMN.

Smart Objects Identification System for Robotic Surveillance

Video surveillance is an active research topic in computer vision. In this paper, humans and cars identification technique suitable for real time video surveillance systems is presented. The technique we proposed includes background subtraction, foreground segmentation, shadow removal, feature extraction and classification. The feature extraction of the extracted foreground objects is done via a new set of affine moment invariants based on statistics method and these were used to identify human or car. When the partial occlusion occurs, although features of full body cannot be extracted, our proposed technique extracts the features of head shoulder. Our proposed technique can identify human by extracting the human head-shoulder up to 60%---70% occlusion. Thus, it has a better classification to solve the issue of the loss of property arising from human occluded easily in practical applications. The whole system works at approximately 16---29 fps and thus it is suitable for real-time applications. The accuracy for our proposed technique in identifying human is very good, which is 98.33%, while for cars' identification, the accuracy is also good, which is 94.41%. The overall accuracy for our proposed technique in identifying human and car is at 98.04%. The experiment results show that this method is effective and has strong robustness.

Real Time Video Surveillance System

The significance of Real Time Video Surveillance System has been elevated due to increased importance in safety and security. The Real Time Video Surveillance System is capable of detecting, classifying and tracking objects of interest (human), by recognizing motion of a walking person in video, which can lead to a useful system. This is done by generating notification to an authoritative person by displaying the recorded face of the threat. Gait Analysis helps us to identify a person by their walking style, which also helps for individual recognition i.e. for performing feature extraction. To accomplish this we have used a new spatio-temporal gait representation called as Gait Energy Image (GEI). GEI represents a human motion sequence in a single image while preserving temporal information. For human recognition, statistical GEI feature matching is used to overcome limitation of training templates. Also to reduce dimensionality problem of GEI’s, we have used two approaches, namely Principal Component Analysis (PCA) and its variants Multiple Discriminant Analysis (MDA). These approaches helped us to achieve good performance over the existing approaches.

New Method Based on Multi-Threshold of Edges Detection in Digital Images

Edges characterize object boundaries in image and are therefore useful for segmentation, registration, feature extraction, and identification of objects in a scene. Edges detection is used to classify, interpret and analyze the digital images in a various fields of applications such as robots, the sensitive applications in military, optical character recognition, infrared gait recognition, automatic target recognition, detection of video changes, real-time video surveillance, medical images, and scientific research images. There are different methods of edges detection in digital image. Each one of these methods is suited to a particular type of images. But most of these methods have some defects in the resulting quality. Decreasing of computation time is needed in most applications related to life time, especially with large size of images, which require more time for processing. Threshold is one of the powerful methods used for edge detection of image. In this paper, We propose a new method based on different Multi-Threshold values using Shannon entropy to solve the problem of the traditional methods. It is minimize the computation time. In addition to the high quality of output of edge image. Another benefit comes from easy implementation of this method.