Cloud-based Video Surveillance Research Articles

Attention-Guided HDR Reconstruction for Enhancing Smart City Applications

In the context of smart city development, video surveillance serves as a critical component for maintaining public safety and operational efficiency. However, traditional surveillance systems are often constrained by a limited dynamic range, leading to the loss of essential image details. To address this limitation, this paper introduces HDRFormer, an innovative framework designed to enhance high dynamic range (HDR) image quality in edge–cloud-based video surveillance systems. Leveraging advanced deep learning algorithms and Internet of Things (IoT) technology, HDRFormer employs a unique architecture comprising a feature extraction module (FEM) and a weighted attention module (WAM). The FEM leverages a transformer-based hierarchical structure to adeptly capture multi-scale image information. In addition, the guided filters are utilized to steer the network, thereby enhancing the structural integrity of the images. On the other hand, the WAM focuses on reconstructing saturated areas, improving the perceptual quality of the images, and rendering the reconstructed HDR images with naturalness and color saturation. Extensive experiments on multiple HDR image reconstruction datasets demonstrate HDRFormer’s substantial improvements, achieving up to a 2.7 dB increase in the peak signal-to-noise ratio (PSNR) and an enhancement of 0.09 in the structural similarity (SSIM) compared to existing methods. In addition, the framework exhibits outstanding performance in multi-scale structural similarity (MS-SSIM) and HDR visual difference predictor (HDR-VDP2.2). The proposed method not only outperforms the existing HDR reconstruction techniques but also offers better generalization capabilities, laying a robust foundation for future applications in smart cities.

A Fog-Assisted Framework for Intelligent Video Preprocessing in Cloud-Based Video Surveillance as a Service

Video preprocessing is one of the vital steps before extracting useful information from surveillance videos. This article identifies the key issue in cloud-based video surveillance as a service (VSaaS). If the recording environment is almost static and storing 24/7 video recording leads to additional storage, computational and network complexity to the cloud user. To address this problem, the existing architecture of the VSaaS is thoroughly studied and proposed a fog-assisted framework for preprocessing the incoming video. The two important functionalities of this framework are intelligent frame-selection and frame-reconstruction. During frame-selection, the lightweight background subtraction algorithm based on random projection with structural similarity index manages to effectively select the necessary frames to store in the cloud. This framework also provides an option for the user to successfully reconstruct the full-length video using frame inbetweening method. The proposed algorithm is tested with CDNET-2014 and LASIESTA data. The results are evaluated in-terms of running time, selected frames per video, percentage of reduction and quality of video experiencing. The result shows that the proposed frame-selection manages to reduce nearly 60% of storage requirement for the CDNET-2014 data. The frame reconstruction algorithm reconstructs the full-length video without affecting the quality of video experiencing.

Performance enhancement of PRNU-based source identification for smart video surveillance

This paper introduces a current signal-based source verification (SSV) system for images in video surveillance networks including the cloud. Using a signal, the well-known photo-response non-uniformity (PRNU) can be used, which is unique and intrinsic in every digital image taken by a source camera, like fingerprints. The SSV system using PRNU has proved before to be useful for reliable video source identification in both network- and cloud-based video surveillance. However, in the era of smart living, security video systems have become part of the IoT devices which typically have limited resources such as low computation, power, storage and memory. To address these problems in the IoT applications, the effects of I-frames only and infra-red night scenes are studied as well as two proposed approaches for the SSV system. Then a hybrid version of the SSV scheme is further suggested, in combination with the best approach using averaged noise residues (for reduced false positive rate), and a recent technique using spatial domain averaged frames (for reduced computational complexity). The enhanced performance of the improved SSV system for smart video surveillance has been verified through tests.

Privacy-preserving Motion Detection for HEVC-compressed Surveillance Video

In the cloud era, a large amount of data is uploaded to and processed by public clouds. The risk of privacy leakage has become a major concern for cloud users. Cloud-based video surveillance requires motion detection, which may reveal the privacy of people in a surveillance video. Privacy-preserving video surveillance allows motion detection while protecting privacy. The existing scheme [ 25 ], designed to detect motion on encrypted and H.264-compressed surveillance videos, does not work well on more advanced video compression schemes such as HEVC. In this article, we propose the first motion detection method on encrypted and HEVC-compressed videos. It adopts a novel approach that exploits inter-prediction reference relationships among coding blocks to detect motion regions. The partition pattern and the number of coding bits of each detection block used in prior art are also used to help detect motion regions. Spatial and temporal consistency of a moving object and Kalman filtering are applied to segment connected/merged motion regions, remove noise and background motions, and refine trajectories and shapes of detected moving objects. Experimental results indicate that our detection method achieves high detection recall, precision, and F1-score for surveillance videos of both high and low resolutions with various scenes. It has a similarly high detection accuracy on encrypted and HEVC-compressed videos as that of the existing motion detection method [ 25 ] on encrypted and H.264-compressed videos. Our proposed method incurs no bit-rate overhead and has a very low computational complexity for both motion detection and encryption of HEVC videos.

IoT-based smart healthcare video surveillance system using edge computing

Managing distributed smart surveillance system is identified as a major challenging issue due to its comprehensive aggregation and analysis of video information on the cloud. In smart healthcare applications, remote patient and elderly people monitoring require a robust response and alarm alerts from surveillance systems within the available bandwidth. In order to make a robust video surveillance system, there is a need for fast response and fast data analytics among connected devices deployed in a real-time cloud environment. Therefore, the proposed research work introduces the Cloud-based Object Tracking and Behavior Identification System (COTBIS) that can incorporate the edge computing capability framework in the gateway level. It is an emerging research area of the Internet of Things (IoT) that can bring robustness and intelligence in distributed video surveillance systems by minimizing network bandwidth and response time between wireless cameras and cloud servers. Further improvements are made by incorporating background subtraction and deep convolution neural network algorithms on moving objects to detect and classify abnormal falling activity monitoring using rank polling. Therefore, the proposed IoT-based smart healthcare video surveillance system using edge computing reduces the network bandwidth and response time and maximizes the fall behavior prediction accuracy significantly comparing to existing cloud-based video surveillance systems.

Robust Privacy-Preserving Motion Detection and Object Tracking in Encrypted Streaming Video

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of 4×4 pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.

Multimedia learning platform development and implementation based on cloud environment

In recent years, with the rapid development of cloud computing, the massive storage capacity and massive computing power of cloud computing have brought new development opportunities to the security field. The traditional tourism professional multimedia teaching platform is also difficult to meet the current massive storage video. The demand for data, although there are work has been tried to deploy in the cloud environment, but a versatile platform is still an industry challenge. This paper designs and implements a cloud-based video surveillance platform based on the real-time, security, bandwidth dependence and high transmission cost of the multimedia professional teaching field. The cloud storage technology is used to solve the heterogeneity of video data. Use the cloud to solve the scalability of the platform. Then use H.264 video coding standard and RTSP video real-time transmission protocol to solve the problem of bandwidth dependence and real-time, and propose to build an embedded sensor network to carry out identity identification and centralized control separately. The network is dynamically tied to IP. The fixed video transmission method indirectly solves the instability of dynamic IP, and makes full use of FTTH resources, reducing the user cost.

Replicas Strategy and Cache Optimization of Video Surveillance Systems Based on Cloud Storage

With the rapid development of video surveillance technology, especially the popularity of cloud-based video surveillance applications, video data begins to grow explosively. However, in the cloud-based video surveillance system, replicas occupy an amount of storage space. Also, the slow response to video playback constrains the performance of the system. In this paper, considering the characteristics of video data comprehensively, we propose a dynamic redundant replicas mechanism based on security levels that can dynamically adjust the number of replicas. Based on the location correlation between cameras, this paper also proposes a data cache strategy to improve the response speed of data reading. Experiments illustrate that: (1) our dynamic redundant replicas mechanism can save storage space while ensuring data security; (2) the cache mechanism can predict the playback behaviors of the users in advance and improve the response speed of data reading according to the location and time correlation of the front-end cameras; and (3) in terms of cloud-based video surveillance, our proposed approaches significantly outperform existing methods.

A security framework for cloud-based video surveillance system

Utilizing cloud services in running large-scale video surveillance systems is not uncommon. However, special attention should be given to data security and privacy because, typically, data travels over insecure public networks. In this work, we propose an end-to-end security framework for a cloud-based video surveillance system that supports a large number of cameras. Our framework provides mutual authentication, session key management, data confidentiality, and data integrity. Consequently, encrypted video frames can only be sourced from authenticated cameras and only destined to authenticated cloud devices where the integrity of such frames can also be verified against potential change. As video streaming is a very delay-sensitive application, we study different variations of the proposed framework to find security options that achieve the best trade-off between the added delay and the security of the system.

Workload scheduling for Nokkhum video surveillance as a service

Video Surveillance Systems (VSS) on the Internet as known as Video Surveillance as a Service (VSaaS) or Cloud based Video Surveillance (CVS) systems. Video processing workload analysis has usually employed only one category of static video processing attribute, such as a frame rate with a single frame size on the same computing node specification, but VSaaS must handle a variety of video processing attributes. Also, in a static workload, it is difficult to identify the resource consumption of video processing attributes, especially involving a combination of frame rates and sizes on different computing nodes on virtual or physical machines. Consequently, it is difficult to place a task on a computing node if the resource usage information is unknown to the scheduler. In this paper, the video processing workload characteristics utilize various parameters, such as the type of video processing task, frame rate, frame size, and compute node specification. The analysis results have helped us to design a scheduler that supports different computing node specifications. We explore video processing workload for testing resource usage capacity in several computing nodes, and collect information for the scheduler’s estimation. This paper also proposes a resource estimation module for predicting the video processing resource usage for a new video processing task when there is no matching or close estimation. Furthermore, we suggest scheduler criteria for optimizing system resource usage.

Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

The next generation video surveillance systems are expected to face challenges in providing computation support for an unprecedented amount of video streams from multiple video cameras in a timely and scalable fashion. Cloud computing offers huge computation resources for large-scale storage and processing on demand, which are deemed suitable for video surveillance tasks. Cloud also provides quality of service guaranteed hardware and software solutions with the virtual machine (VM) technology using a utility-like service costing model. In cloud-based video surveillance context, the resource requests to handle video surveillance tasks are translated in the form of VM resource requests, which in turn are mapped to VM resource allocation referring to physical server resources hosting the VMs. Due to the nature of video surveillance tasks, these requests are highly time-constrained, heterogeneous and dynamic in nature. Hence, it is very challenging to actually manage the cloud resources from the perspective of VM resource allocation given the stringent requirements of video surveillance tasks. This paper proposes a computation model to efficiently manage cloud resources for surveillance tasks allocation. The proposed model works on optimizing the trade-off between average service waiting time and long-term service cost, and shows that long-term service cost is inversely proportional to high and balanced utilization of cloud resources. Experiments show that our approach provides a near-optimal solution for cloud resource management when handling the heterogeneous and unpredictable video surveillance tasks dynamically over next generation network.

Design and Implementation of a Prototype Cloud Video Surveillance System

Cloud-based video surveillance systems, as a new cloud computing service model, are an emerging research topic, both at home and abroad. Current research is mainly focused on exploring applications of the system. This paper proposes a design and implementation method for cloud-based video surveillance systems using the characteristics of cloud computing, such as parallel computing, large storage space, and easy expandability. The system architecture and function modules are built, and a prototype cloud-based video surveillance system is established in a campus network using key technologies, including virtual machine task access control, video-data distributed storage, and database-active communicationmethods. Using the system, the user is able to place a webcam in a location that requires monitoring so that video surveillance can be achieved, and video data can be viewed through a browser. The system has the following advantages: low investment and maintenance cost, high portability, easily extendable, superior data security, and excellent sharing. As a private cloud server in the campus network, the system is able to not only provide convenient video surveillance services, but it can also be an excellent practical experimental platform for cloud computing-related research, which carries outstanding application value.