Cloud-Based Infiltration Detection System for Military Purposes

2015 ◽  
pp. 358-386
Author(s):  
ChandraMani Sharma ◽  
Deepika Sharma ◽  
Harish Kumar
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Detection System ◽  
Traditional Approach ◽  
Visual Surveillance ◽  
Video Data ◽  
Video Footage ◽  
Human Operators ◽  
Defense Studies ◽  
Border Surveillance

Massive military manpower is deployed on borders to keep a vigilant eye on possible infiltration from neighboring countries. This traditional approach is prone to errors because of human factors. To make border surveillance more effective, countries have installed CCTV cameras on borders but generally such systems are passive in nature and require human operators to keep an eye on the captured video footage. This chapter describes a cloud based approach for infiltration detection in border defense environments. The processing of video data, in cloud, and the real-time response are the factors that make system suitable for military purposes. As far as affordability is concerned, the governments can easily bear expense of establishing a private cloud for implementing visual surveillance. This chapter represents pertinent research of authors in the field of visual surveillance as well as other state-of-the-art breakthroughs in this area. The chapter is of multi-disciplinary significance in the field of cloud computing, video & image processing, behavioral sciences, and defense studies.

Cloud-Based Infiltration Detection System for Military Purposes

Biometrics ◽  
2017 ◽  
pp. 573-603
Author(s):  
ChandraMani Sharma ◽  
Deepika Sharma ◽  
Harish Kumar
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
Detection System ◽  
Traditional Approach ◽  
Visual Surveillance ◽  
Video Data ◽  
Video Footage ◽  
Human Operators ◽  
Defense Studies ◽  
Border Surveillance

Massive military manpower is deployed on borders to keep a vigilant eye on possible infiltration from neighboring countries. This traditional approach is prone to errors because of human factors. To make border surveillance more effective, countries have installed CCTV cameras on borders but generally such systems are passive in nature and require human operators to keep an eye on the captured video footage. This chapter describes a cloud based approach for infiltration detection in border defense environments. The processing of video data, in cloud, and the real-time response are the factors that make system suitable for military purposes. As far as affordability is concerned, the governments can easily bear expense of establishing a private cloud for implementing visual surveillance. This chapter represents pertinent research of authors in the field of visual surveillance as well as other state-of-the-art breakthroughs in this area. The chapter is of multi-disciplinary significance in the field of cloud computing, video & image processing, behavioral sciences, and defense studies.

The Study of Image Processing Method Based on OpenCV and USB Industrial Camera

2012 ◽  
Vol 220-223 ◽  
pp. 2721-2725 ◽  
Author(s):  
Zhen Feng Huang ◽  
Hai Ping Chen ◽  
Li Li Cheng
Keyword(s):  
Image Processing ◽  
Visual Detection ◽  
Detection System ◽  
Processing Method ◽  
Video Data ◽  
Processing Software ◽  
Industrial Cameras ◽  
The One ◽  
Library Function ◽  
Speed And Accuracy

To improve the speed and accuracy of visual detection system, image processing software has been developed. However, it found two problems in the development of the system. The one is OpenCV library function can't directly obtain video data flow from the USB industrial cameras with non-V4L interface on the Linux platform. The other one is the IplImage structure images can't immediately be showed in Qt control. Based on these problems, this paper proposes a new solution, the shared-streams method, and offers the program codes to implement this method. Finally, after experiment and contrast, the results indicate that shared-streams approach has a certain feasibility and superiority.

Traffic incident detection system based on video image processing

2008 ◽  
Vol 28 (7) ◽  
pp. 1886-1889 ◽  
Author(s):  
Qin WANG ◽  
Shan HUANG ◽  
Hong-bin ZHANG ◽  
Quan YANG ◽  
Jian-jun ZHANG
Keyword(s):  
Image Processing ◽  
Detection System ◽  
Video Image ◽  
Incident Detection ◽  
Traffic Incident ◽  
Video Image Processing

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

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.

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Massive Image Treatment System Based on Cloud Computing Platform

2014 ◽  
Vol 687-691 ◽  
pp. 3733-3737
Author(s):  
Dan Wu ◽  
Ming Quan Zhou ◽  
Rong Fang Bie
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Large Scale ◽  
Processing System ◽  
Virtual Space ◽  
Image Processing System ◽  
Computing Platform ◽  
Simulation Calculation ◽  
Computer Resources

Massive image processing technology requires high requirements of processor and memory, and it needs to adopt high performance of processor and the large capacity memory. While the single or single core processing and traditional memory can’t satisfy the need of image processing. This paper introduces the cloud computing function into the massive image processing system. Through the cloud computing function it expands the virtual space of the system, saves computer resources and improves the efficiency of image processing. The system processor uses multi-core DSP parallel processor, and develops visualization parameter setting window and output results using VC software settings. Through simulation calculation we get the image processing speed curve and the system image adaptive curve. It provides the technical reference for the design of large-scale image processing system.

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