A mobile video surveillance system with intelligent object analysis

We develop and study adaptive rate scheduling mechanisms over cellular wireless networks, as used for the unicast provision of video streams to client mobiles at variable quality of experience (QoE) levels. Under service type I, mobile users receive video streams at a QoE level that is not lower than a specified value. In addition, mobile users that experience sufficiently high communications channel quality levels, may be provided video streams at higher video quality levels. Under service type II, mobile clients receive their video streams at QoE levels that are based on their recorded signal to noise and interference (SINR) levels. Resource allocations among mobiles are however subjected to absolute and proportional fairness objectives. We employ a proxy video manager and resource controller that is located at (or associated with) the base station node. The manager intercepts a channel quality indicator (CQI) message reported by a mobile client, using it to determine the QoE level at which a requested video stream will be provided. It then selects the proper source and channel encoding schemes to be used for producing and transmitting a compressed version of the stream. To regulate inter-cell signal interference, we examine the joint employment of a number of different spectral-reuse and fractional frequency reuse (FFR) scheduling schemes. To illustrate the use of our models to configure system parameters, we consider a performance metric that incorporates a will-topay utility function. We develop analytical techniques for the modeling, analysis and design of such systems. We confirm the precision of these models through the conduct of simulation analyses. Also, we show that, under certain system configurations, FFR-based schemes can lead to substantial enhancement of the system’s performance behavior.

Today, the main tool of effective management at both the local, regional, and state levels is the availability of appropriate information support. This especially applies to the security and defense sector, where the availability of the necessary information support is the main requirement for the effective response of departmental bodies (relevant law enforcement agencies) to crisis situations arising both in society and the state as a whole. In connection with this, the role of video information support as a means for prompt decision-making is significantly increased. This is due to the fact that the key principles of the implementation of video information provision are timeliness (operational) and reliability. For this purpose, stationary and mobile photo and video surveillance systems are quite actively used. The use of the latter is closely related to the aviation segment — unmanned aerial vehicles and complexes, the role of which is increased by the presence of such properties as scale and mobility. However, at the same time, the following problematic factors arise related to the use of wireless communication technologies for data delivery to the final addressee: an imbalance between the ever-increasing volumes of data and the bandwidth of data transmission channels; the influence of obstacles arising in the process of video data delivery on the level of reliability of the reconstructed video image. It should be noted that the use of existing methods of interference-resistant coding to solve the above-mentioned problems leads to a significant increase in the volume of video data, which is critical in the conditions of using wireless communication technologies — video is an information resource transmitted with significant time delays. For this purpose, a method of video data reconstruction is being developed based on the use of identifiers (markers) of uneven code structures assigned to the elements of clusters formed as a result of the restructuring of the information space by structural feature. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource. A distinctive feature of the developed method is the independent decompositional statistical decoding of individual code subsets according to structural features. This ensures, due to the use of additional service information, the localization of errors in the process of data reconstruction of the video information resource.

In real time object tracking is an important task in various surveillance application. Nowadays, surveillance system very common in offices, collage campus, shopping malls etc. In this paper, an automated video surveillance system presented. Althrough tracking and counting system are commercially available today; there is need for further reaserach to address the challenges of real world scenarios. There is lot of surveillance cameras installed around us but there are no means to monitor all of them continuously. It is necessary to develop a computer vision based technologies that automatically process those images in order to detect situations or unusual behavior. In order to solve the problem of poor real time tracking performance using convolutional neural network. This paper proposes a fast and accurate real time video detection and tracking algorithm. Keyword: - Video surveillance system, Moving object detection, Tracking, Background subtraction. I. Introduction Real time human body detection and tracking in indoor environment deals with the concept of higher level application of image processing and computer vision. The following key components of identify to path of real time tracking are extracting a feature, background image subtraction. If two similar color objects come to close the tracker of one object can jump to associated object. The problem is called hijacking problem. Drifting problem occur when an object abruptly changes its direction to reverse. In that case it’s become very difficult to track the object because the motion model does not work. An object tracking can be understood as the problem of finding the path (i.e. Trajectory).

Moving objects have been detected using various object detection techniques. Two categories for moving object detection techniques are frame differencing based and background subtraction based. These techniques are limited by camera scene complexity, light conditions, video type etc. Frame differencing based techniques process videos faster compared to background subtraction based techniques. Frame differencing based techniques detects only the boundary of the moving object and may fail for slow moving objects. These techniques for moving object detection can be improved by using sound data as most video recording cameras are equipped with a microphone. Sounds from human footsteps can be recorded with video and used with frame differencing techniques to improve moving object detection results. Camera microphones also record background noise with other background sound. This noisy data has been filtered out using the Fourier transform. When peak locations for each footstep sound are determined, and a Full Width at Half Maxima is computed for each peak, the number of frames within this width are counted, these frames are verify the presence of a moving object.

Moving object detection using modified GMM based background subtraction

Automatic moving object detection is essential for various computer vision applications like video surveillance systems. Many previous detection methods work for usually low-res video sequences under certain constraints and are based on background learning and/or pixel-level motion analysis or they focus on detecting particular objects. We introduce a hybrid moving object detection scheme with motion-color features, followed by a statistical optimization step to increase the accuracy of the boundaries of the detected objects in hi-resolution video sequences taken in the presence of camera motions such as camera vibrations. Motion analysis involves both the motion vector information extracted from a reference H.264 decoder and a moving-edge map in order to produce an overestimate of the moving object blobs. Pyramid color segmentation connecting multiple components that might be under different motions is performed to extract the solid bodies of moving object blobs with accurate boundaries. Results from motion and color analysis are fused and a region-growing technique based on the blobs' Gaussian distribution of its RGB information is performed to further refine moving blobs. Results are shown to demonstrate the accuracy of our method.

In view of the problems existed in moving object detection in video surveillance system, background subtraction method is adopted and combined with Surendra algorithm for background modeling, an algorithm of detecting moving object from video is proposed, and OpenCV programming is adopted in Visual c ++ 6.0 for implementation. Experimental results indicate that the algorithm can accurately detect and identify moving object in video by reading the image sequence of surveillance video, the validity of the algorithm is verified.

In recent scenario, video surveillance systems are important to monitor security sensitive areas such as banks, crowded public places and borders. Hence, Object detection in such video surveillance is an important task. Research work to detect an object with steady background is carried out and upto some extent it is easy but with moving background it is still challenging. Object detection is complicated as the camera motion and object motion are mixed. Motion vectors, represents flow of a moving object, are obtained using Lucas - kanade optical flow algorithm for moving object detection with complex background. These flow vectors are quantized using a predefined threshold to decide whether a pixel is a part of an object or a background. We have used bilateral filter as a pre-processing step due to which accurate and fast detection of the object is achieved.

To detect moving objects from a video sequence is a fundamental and critical task in many computer vision applications. For China high-speed railway transport hub video surveillance system, we need a stable, fast and accurate moving object detection method to promptly find the congestion of passenger flow and other dangerous in hub. Through the comparative study on moving object detection, we find the average/median filtering is the most appropriate method for China high-speed railway transport hub video surveillance.

This thesis addresses the open research problem of efficiently use Unmanned Aerial Vehicles to mitigate and eventually recover Cellular Networks in extreme conditions, such as congestion or natural disasters. Small scale UAVs are at a mature stage of development and can today carry lightweight commercial micro-cells with smaller form factor. The mobile micro-cell can be used to offload users from a congested micro cell to optimize the bandwidth usage for applications such as video streaming. The delay mitigation problem for video streaming applications in congested cellular macro-cells by using a mobile micro-cell mounted on an UAV is studied. Firstly the thesis discusses algorithms and comprehensive design criteria for user offload selection (selecting what users need to be offloaded to the micro-cell) and drone positioning (selecting the position of the UAV that minimizes the network delay). The effectiveness of the proposed criteria is evaluated through extensive performance evaluation. We show that the performance increases consistently in terms of bandwidth requests mitigation and average delay reduction under different system configurations. Secondly a prototype of an Aerial Base Station compatible with 5G architecture is proposed. The proposed framework allows for on-the-fly drone repositioning based on rigorous optimization techniques using real-time network metrics to enhance users' service. Experimental results demonstrate that UABSs are able to autonomously reposition themselves based on cellular network metrics and to improve network performance. Finally the HIRO-NET robotic framework is presented. HIRO-NET is an emergency infrastructure-less network tailored to address the problem of providing connectivity in the immediate aftermath of a natural disaster, where no cellular or wide area network is operational and no Internet access is available. HIRO-NET establishes a two-tier wireless mesh network where the Lower Tier connects nearby survivors in a self-organized mesh via Bluetooth Low Energy and the Upper Tier creates long-range VHF links between autonomous robots exploring the disaster stricken area. In the event of an Internet connection still being available to some user, HIRO-NET is able to opportunistically share and provide access to low data-rate services (e.g. Twitter, Gmail) to the whole network. Results show that the Lower Tier always reaches network convergence and the Upper Tier can virtually extend HIRO-NET functionalities to the range of a small metropolitan area.--Author's abstract

Nowadays, video streaming has became one of the most popular multimedia applications and will be the dominant data traffic in cellular networks. This is a real challenge for mobile network operators because the cellular base stations will be overloaded. One promising solution to offload mobile traffic from the base stations is the use of Device-to-Device (D2D) communications. Each mobile phone can use both cellular and Wi-Fi network interfaces to download video segments either from the Internet or from a neighboring device. However, it is crucial to improve the reliability of D2D communications to maximize the offloading benefits. In this paper, we use network coding to address the reliability in cooperative D2D-based transmission of video streaming. We have implemented Random Linear Network Coding (RLNC) on the CoopStreaming system and evaluate the offloading gain. The experimental results show that the use of network coding has significantly improved the reliability of D2D communications among neighboring devices and therefore increased the cellular offloading gain.

Internet video streaming is a hot topic in multimedia systems. A large variety of devices (computers, mobile phones, TVs, etc.) are connected to the Internet via wired or wireless networks and are capable of receiving and decoding HD video content. To enable new services like HD video streaming (e.g., online video rental), the Internet’s infrastructure was enhanced. But the Internet is still a best-effort network, which does not implement quality-of-service or admission control, resulting in time-varying bandwidth and packet delay, packet loss and network congestion. Because video streaming accounts for a considerable amount of the Internet’s traffic, video streaming needs additionally to be congestion-aware, to avoid a congestion collapse of the Internet. The Transmission Control Protocol (TCP) can adapt to changing network conditions and is currently the de facto standard protocol for congestion-aware and reliable data transmission in the Internet. This fact gave TCP-based video streaming a huge momentum. Consequently, this thesis investigates TCP-based adaptive video streaming for the Internet. The main goal is to provide a solution for congestion-aware video streaming, while still being able to achieve a reasonable performance in error-prone networks. To complement existing work on congestion-aware adaptive streaming, this thesis makes six contributions. (1) The baseline performance of TCP-based adaptive streaming is identified by means of an evaluation of different adaptive streaming approaches. The results represent a reference for further investigations. (2) An investigation on the influence of TCP’s behavior in presence of packet loss on the video streaming performance. (3) To overcome the shortcomings of TCP-based video streaming (single TCP connections fail to deliver a good performance in case of packet loss), a new approach to video streaming based on multiple request-response streams was introduced. The novelty of this system is that it is able to make use of multiple HTTP-based request-response streams while still providing TCP-friendliness. (4) A performance model of the HTTP-based request-response streams was developed, to estimate the influence of the system parameters and the network characteristics on the throughput performance. (5) A comprehensive evaluation of the HTTP-based request-response streams under diverse network conditions was conducted, to validate the model’s estimations. Additionally, the TCP-friendliness was evaluated, showing that request-response streaming systems can be configured to achieve TCP-friendliness. (6) A cellular network with high bandwidth fluctuations and RTTs was used to investigate the performance of the request-response streaming system in a mobile video streaming scenario. The results indicate that the streaming system can make good use of the available bandwidth, while the number of quality switches is kept low. While aggregating multiple TCP connections to improve the TCP streaming performance is quite common, usually the improvement comes at the cost of high deployment effort. By placing the streaming logic at the client, request-response streams can avoid this complexity. Additionally, this client-driven approach responds faster to changing network conditions and enables easy recovery from connection stalls or aborts, because the control loop is at the client. To improve the network efficiency and the scalability in terms of number of clients served, HTTP-based request-response streams can utilize HTTP proxies and caches.

Performance evaluation of DTN protocols to deliver sms in dense mobile network : Empirical proofs

We present one of the key-enabling technologies in 5G wireless networks, i.e., Device-to-Device (D2D) communication. Furthermore, we discus some of our work in utilizing D2D communication in one of the most bandwidth-demanding applications in 5G networks, i.e., video streaming. D2D communication, introduced by the LTE-Advanced (LTE-A) standard, allows direct communication between devices in cellular networks. We review some of the work in the literature on D2D communication and its applications in video streaming. We also discus an architecture that provides Dynamic Adaptive Streaming over HTTP (DASH)—based Peer-to-Peer (P2P) video streaming in cellular networks. The architecture employs Base-Station (BS)—assisted D2D video transmission in cellular networks for direct exchange of video contents among users. We evaluate the performance of the proposed architecture in terms of many video streaming Quality-of-Experience (QoE) metrics.KeywordsDevice-to-device communicationVideo streaming5G networksDABAST

Moving target detection and tracking algorithm as the core issue of computer vision and human-computer interaction is the first step of intelligent video surveillance system. Through comparing temporal difference method and background subtraction, a moving object detection and tracking algorithm based on background subtraction under static background is proposed, in order to quickly and accurately detect and identify the moving object in the intelligent monitoring system. In this algorithm, firstly, we use background acquisition method to receive the background image, then use the current frame image and the received background image to perform background subtraction in order to extract foreground object information and receive the difference image; secondly, we use threshold segmentation and morphology image processing to process the difference image in order to eliminate noises and receive the clear binary moving object image; finally, we use the centroid tracking method to track and mark the moving object. Experimental results show that the algorithm can effectively and quickly detect and track moving object from video sequence under static background. This algorithm is easily realized and has good real-time and robust, which is automated and self triggered for background updating. The algorithm can be used in driver assistance systems, motion capture, virtual reality and other fields.