scholarly journals Post Event Investigation of Multi-stream Video Data Utilizing Hadoop Cluster

2018 ◽  
Vol 8 (6) ◽  
pp. 5089
Author(s):  
Jyoti Parsola ◽  
Durgaprasad Gangodkar ◽  
Ankush Mittal
Keyword(s):  
Video Data ◽  
Video Frame ◽  
Surveillance Video ◽  
Pixel Resolution ◽  
Data Intensive ◽  
Video Frames ◽  
Time Period ◽  
Speed Up ◽  
Hadoop Cluster ◽  
Data Intensive Applications

<p>Rapid advancement in technology and in-expensive camera has raised the necessity of monitoring systems for surveillance applications. As a result data acquired from numerous cameras deployed for surveillance is tremendous. When an event is triggered then, manually investigating such a massive data is a complex task. Thus it is essential to explore an approach that, can store massive multi-stream video data as well as, process them to find useful information. To address the challenge of storing and processing multi-stream video data, we have used Hadoop, which has grown into a leading computing model for data intensive applications. In this paper we propose a novel technique for performing post event investigation on stored surveillance video data. Our algorithm stores video data in HDFS in such a way that it efficiently identifies the location of data from HDFS based on the time of occurrence of event and perform further processing. To prove efficiency of our proposed work, we have performed event detection in the video based on the time period provided by the user. In order to estimate the performance of our approach, we evaluated the storage and processing of video data by varying (i) pixel resolution of video frame (ii) size of video data (iii) number of reducers (workers) executing the task (iv) the number of nodes in the cluster. The proposed framework efficiently achieve speed up of 5.9 for large files of 1024X1024 pixel resolution video frames thus makes it appropriate for the feasible practical deployment in any applications.</p>

scholarly journals Composite cellular automata based encryption method applied to surveillance videos

DYNA ◽  
2020 ◽  
Vol 87 (213) ◽  
pp. 212-221
Author(s):  
Luis Miguel Cortés Martinez ◽  
Luz Deicy Alvarado Nieto ◽  
Edilma Isabel Amaya Barrera
Keyword(s):  
Cellular Automata ◽  
Scientific Development ◽  
Video Frame ◽  
Surveillance Video ◽  
Surveillance Videos ◽  
Video Frames ◽  
And Performance ◽  
Reversible Cellular Automata ◽  
Encryption Method ◽  
Development Center

This work is part of the research project “Encryption Models Based on Chaotic Attractors” institutionalized in the Research and Scientific Development Center of the Universidad Distrital Francisco José de Caldas. In this paper, a symmetric encryption method for surveillance videos is presented, based on reversible composite cellular automata developed for this purpose. This method takes advantage of reversible cellular automata and elementary rule 30 properties, for efficient regions of interest encryption in surveillance video frames, obtaining an algorithm which experimental results of security and performance are consistent with those reported in current literature. In addition, it allows decryption without loss of information through a fixed size key for each video frame.

scholarly journals POINT CLOUD DERIVED FROMVIDEO FRAMES: ACCURACY ASSESSMENT IN RELATION TO TERRESTRIAL LASER SCANNINGAND DIGITAL CAMERA DATA

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 217-223 ◽  
Author(s):  
P. Delis ◽  
M. Zacharek ◽  
D. Wierzbicki ◽  
A. Grochala
Keyword(s):  
Data Storage ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Histogram Equalization ◽  
Video Data ◽  
Video Frame ◽  
Video Frames

The use of image sequences in the form of video frames recorded on data storage is very useful in especially when working with large and complex structures. Two cameras were used in this study: Sony NEX-5N (for the test object) and Sony NEX-VG10 E (for the historic building). In both cases, a Sony α f&amp;thinsp;=&amp;thinsp;16&amp;thinsp;mm fixed focus wide-angle lens was used. Single frames with sufficient overlap were selected from the video sequence using an equation for automatic frame selection. In order to improve the quality of the generated point clouds, each video frame underwent histogram equalization and image sharpening. Point clouds were generated from the video frames using the SGM-like image matching algorithm. The accuracy assessment was based on two reference point clouds: the first from terrestrial laser scanning and the second generated based on images acquired using a high resolution camera, the NIKON D800. The performed research has shown, that highest accuracies are obtained for point clouds generated from video frames, for which a high pass filtration and histogram equalization had been performed. Studies have shown that to obtain a point cloud density comparable to TLS, an overlap between subsequent video frames must be 85&amp;thinsp;% or more. Based on the point cloud generated from video data, a parametric 3D model can be generated. This type of the 3D model can be used in HBIM construction.

scholarly journals A Low-Complex Frame Rate Up-Conversion with Edge-Preserved Filtering

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 156 ◽  
Author(s):  
Ran Li ◽  
Wendan Ma ◽  
Yanling Li ◽  
Lei You
Keyword(s):  
Computational Complexity ◽  
Motion Vector ◽  
Frame Rate ◽  
Video Frame ◽  
Continuous Increase ◽  
Video Frames ◽  
Motion Vector Field ◽  
Frequency Components ◽  
Speed Up ◽  
Frame Rate Up Conversion

The improvement of resolution of digital video requires a continuous increase of computation invested into Frame Rate Up-Conversion (FRUC). In this paper, we combine the advantages of Edge-Preserved Filtering (EPF) and Bidirectional Motion Estimation (BME) in an attempt to reduce the computational complexity. The inaccuracy of BME results from the existing similar structures in the texture regions, which can be avoided by using EPF to remove the texture details of video frames. EPF filters out by the high-frequency components, so each video frame can be subsampled before BME, at the same time, with the least accuracy degradation. EPF also preserves the edges, which prevents the deformation of object in the process of subsampling. Besides, we use predictive search to reduce the redundant search points according to the local smoothness of Motion Vector Field (MVF) to speed up BME. The experimental results show that the proposed FRUC algorithm brings good objective and subjective qualities of the interpolated frames with a low computational complexity.

Research on Edge Detection Algorithm for Caption Extraction from Video Frames

2012 ◽  
Vol 190-191 ◽  
pp. 1040-1043
Author(s):  
Jin Wu
Keyword(s):  
Edge Detection ◽  
Test Object ◽  
Video Retrieval ◽  
Detection Algorithm ◽  
Video Data ◽  
Experimental Results ◽  
Video Frame ◽  
Edge Detection Algorithm ◽  
Video Frames ◽  
Caption Text

Text area extraction from video caption has become an important tool for content-based video retrieval. Test object is frame data intercepted from video data. This paper proposes an algorithm of edge detection to extract caption text that embedded in the video frame by grayed processing. Experimental results have shown that the proposed approach is very effective in text area detection.

scholarly journals Fast surveillance video indexing & retrieval with WiFi MAC address tagging

2019 ◽  
Vol 16 (1) ◽  
pp. 473
Author(s):  
K. L. Tan ◽  
K. C. Lim
Keyword(s):  
Video Camera ◽  
Video Indexing ◽  
Surveillance Video ◽  
Video Streams ◽  
Surveillance Camera ◽  
Camera Systems ◽  
Security Officers ◽  
Retrieval Technique ◽  
Video Frames ◽  
Speed Up

<span>Conventional public safety surveillance video camera systems required 24/7 monitoring of security officers with video wall display installed in the control room. When a crime or incident is reported, all the recorded surveillance video streams nearby the incident area are playback simultaneously on video wall to help locate the target person. The security officers can fast forward the video playback to speed up the video search, but it requires massive manpower if there are hundreds of video streams required to be examined on the video wall. One of the possible solutions is through a suitable video indexing and retrieval technique to prioritize the video frames that need to be processed. This paper presents a WiFi sniffer enabled surveillance camera, with 3-stage WiFi frame inspection filter and the use of collected WiFi signal strength for filtering, to tag the collected WiFi MAC addresses to the surveillance video frames according to the time of the MAC address is sniffed. Additional metadata (WiFi MAC address of smartphone) collected during the occurrence of the incident can be used to prioritize the retrieving of surveillance video frames for subsequent image processing. </span>

Multiprocessor transport surveillance video system based on "system on chip" technology

2020 ◽  
Author(s):  
Ш.С. Фахми ◽  
Н.В. Шаталова ◽  
В.В. Вислогузов ◽  
Е.В. Костикова
Keyword(s):  
System On Chip ◽  
Reference Points ◽  
Surveillance Video ◽  
Video Information ◽  
Chip Technology ◽  
Intelligent Video Surveillance System ◽  
Speed Up ◽  
Algorithmic Analysis ◽  
Computational Procedures ◽  
On Chip

В данной работе предлагаются математический аппарат и архитектура многопроцессорной транспортной системы на кристалле (МПТСнК). Выполнена программно-аппаратная реализация интеллектуальной системы видеонаблюдения на базе технологии «система на кристалле» и с использованием аппаратного ускорителя известного метода формирования опорных векторов. Архитектура включает в себя сложно-функциональные блоки анализа видеоинформации на базе параллельных алгоритмов нахождения опорных точек изображений и множества элементарных процессоров для выполнения сложных вычислительных процедур алгоритмов анализа с использованием средств проектирования на базе реконфигурируемой системы на кристалле, позволяющей оценить количество аппаратных ресурсов. Предлагаемая архитектура МПТСнК позволяет ускорить обработку и анализ видеоинформации при решении задач обнаружения и распознавания чрезвычайных ситуаций и подозрительных поведений. In this paper, we propose the mathematical apparatus and architecture of a multiprocessor transport system on a chip (MPTSoC). Software and hardware implementation of an intelligent video surveillance system based on the "system on chip" technology and using a hardware accelerator of the well-known method of forming reference vectors. The architecture includes complex functional blocks for analyzing video information based on parallel algorithms for finding image reference points and a set of elementary processors for performing complex computational procedures for algorithmic analysis. using design tools based on a reconfigurable system on chip that allows you to estimate the amount of hardware resources. The proposed MPTSoC architecture makes it possible to speed up the processing and analysis of video information when solving problems of detecting and recognizing emergencies and suspicious behaviors

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

scholarly journals 6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1709
Author(s):  
Agbotiname Lucky Imoize ◽  
Oluwadara Adedeji ◽  
Nistha Tandiya ◽  
Sachin Shetty
Keyword(s):  
Wireless Communication ◽  
Psychological Health ◽  
Future Research ◽  
Agriculture Education ◽  
Social Psychological ◽  
Research Issues ◽  
Data Intensive ◽  
Wireless Communication Network ◽  
Data Intensive Applications

The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

scholarly journals EZIOTracer

2021 ◽  
Vol 55 (1) ◽  
pp. 88-98
Author(s):  
Mohammed Islam Naas ◽  
François Trahay ◽  
Alexis Colin ◽  
Pierre Olivier ◽  
Stéphane Rubini ◽  
...  
Keyword(s):  
Analysis Framework ◽  
Comprehensive Understanding ◽  
Kernel Space ◽  
Data Intensive ◽  
Storage Performance ◽  
Performance Requirements ◽  
Memory Footprint ◽  
Extreme Performance ◽  
Data Intensive Applications ◽  
Kernel Level

Tracing is a popular method for evaluating, investigating, and modeling the performance of today's storage systems. Tracing has become crucial with the increase in complexity of modern storage applications/systems, that are manipulating an ever-increasing amount of data and are subject to extreme performance requirements. There exists many tracing tools focusing either on the user-level or the kernel-level, however we observe the lack of a unified tracer targeting both levels: this prevents a comprehensive understanding of modern applications' storage performance profiles. In this paper, we present EZIOTracer, a unified I/O tracer for both (Linux) kernel and user spaces, targeting data intensive applications. EZIOTracer is composed of a userland as well as a kernel space tracer, complemented with a trace analysis framework able to merge the output of the two tracers, and in particular to relate user-level events to kernel-level ones, and vice-versa. On the kernel side, EZIOTracer relies on eBPF to offer safe, low-overhead, low memory footprint, and flexible tracing capabilities. We demonstrate using FIO benchmark the ability of EZIOTracer to track down I/O performance issues by relating events recorded at both the kernel and user levels. We show that this can be achieved with a relatively low overhead that ranges from 2% to 26% depending on the I/O intensity.

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