Applications of Intelligent Video Analytics in the Field of Retail Management

2018 ◽  
pp. 42-59
Author(s):  
Harkirat Singh
Keyword(s):  
Computer Vision ◽  
Business Intelligence ◽  
Reliable Information ◽  
Loss Prevention ◽  
Retail Management ◽  
Video Analytics ◽  
Video Cameras ◽  
Video Systems ◽  
Market Trends ◽  
Human Operators

Recent technologies in the market trends have demanded the significant need for the possible solutions to intelligent video cameras that are nothing but intelligent video analytics. Today in the retail industry, intelligent video analytics has gone beyond the tradition of the domain of security and loss prevention by providing retailers insightful business intelligence such as store traffic statistics and queue data. This provides accurate and reliable information by monitoring continuously through a large number of video cameras events that human operators or employees can overlook. This paper gives an overview study of applications of intelligent video analytics in retail management as well as the state-of-the-art computer vision techniques behind them to analyze the data. It clearly demonstrates that the importance of the role that intelligent video systems and analytics play can be found in a variety of applications of intelligent video Analytics in the field of Retail Management.

scholarly journals A Track Geometry Measuring System Based on Multibody Kinematics, Inertial Sensors and Computer Vision

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 683
Author(s):  
José L. Escalona ◽  
Pedro Urda ◽  
Sergio Muñoz
Keyword(s):  
Computer Vision ◽  
Inertial Sensors ◽  
Vision System ◽  
Accurate Method ◽  
Sensor Data ◽  
Measuring System ◽  
Railway Vehicles ◽  
Track Geometry ◽  
Video Cameras ◽  
Measuring Unit

This paper describes the kinematics used for the calculation of track geometric irregularities of a new Track Geometry Measuring System (TGMS) to be installed in railway vehicles. The TGMS includes a computer for data acquisition and process, a set of sensors including an inertial measuring unit (IMU, 3D gyroscope and 3D accelerometer), two video cameras and an encoder. The kinematic description, that is borrowed from the multibody dynamics analysis of railway vehicles used in computer simulation codes, is used to calculate the relative motion between the vehicle and the track, and also for the computer vision system and its calibration. The multibody framework is thus used to find the formulas that are needed to calculate the track irregularities (gauge, cross-level, alignment and vertical profile) as a function of sensor data. The TGMS has been experimentally tested in a 1:10 scaled vehicle and track specifically designed for this investigation. The geometric irregularities of a 90 m-scale track have been measured with an alternative and accurate method and the results are compared with the results of the TGMS. Results show a good agreement between both methods of calculation of the geometric irregularities.

scholarly journals Progress In Design and Applications Of CCD Cameras For Electron Microscopy

1995 ◽  
Vol 3 (10) ◽  
pp. 12-13 ◽  
Author(s):  
Kenneth H. Downing
Keyword(s):  
High Sensitivity ◽  
Ccd Cameras ◽  
Video Cameras ◽  
Quantum Leap ◽  
Fluorescent Screen ◽  
Lower Beam ◽  
Video Systems ◽  
On Line ◽  
Specimen Quality ◽  
Electron Microscopes

Over the last several years the long-awaited revolution in direct-digital readout systems has begun, with the introduction of efficient slow-scan CCD cameras. Earlier, the introduction of video cameras to electron microscopes had brought a quantum leap in the speed and efficiency of carrying out a host of operations. The high sensitivity of the video cameras provided the ability to see the image in much more detail and at a lower beam intensity than had been previously possible by viewing the fluorescent screen. The ability to assess, on line, characteristics such as specimen quality and image focus, even qualitatively, gave feedback to the operator that previously took hours to obtain. Due to the low resolution of these video systems, however, they were rarely useful for data recording.

scholarly journals Business Intelligence and its role to enhance Corporate Performance Management

2013 ◽  
Vol 3 (3) ◽  
pp. 08-15
Author(s):  
Mostafa Medhat Nazier ◽  
Dr. Ayman Khedr ◽  
Assoc. Prof. Mohamed Haggag
Keyword(s):  
Performance Management ◽  
Business Intelligence ◽  
Future Trends ◽  
Large Organization ◽  
Meaningful Information ◽  
Effective Decision ◽  
Analytical Processing ◽  
Market Trends ◽  
Disparate Data ◽  
Effective Decision Making

As every small or large organization requires information to promote their business by forecasting the future trends, information is now the primary tool to understand the market trends and understand their own position in the market comparison to its competitors. Business intelligence is the use of an organizations disparate data to provide meaningful information and analyses to employees, customers, suppliers, and partners for more efficient and effective decision-making. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), data warehouse (DW), statistical analysis, forecasting, and data mining.

Security Applications Using Computer Vision

2013 ◽  
pp. 1093-1110
Author(s):  
Sreela Sasi
Keyword(s):  
Computer Vision ◽  
Power Plant ◽  
Video Surveillance ◽  
Homeland Security ◽  
Port Security ◽  
Video Cameras ◽  
Security Applications ◽  
Wide Range ◽  
Cargo Inspection ◽  
Computer Vision Systems

Computer vision plays a significant role in a wide range of homeland security applications. The homeland security applications include: port security (cargo inspection), facility security (embassy, power plant, bank), and surveillance (military or civilian), et cetera. Video surveillance cameras are placed in offices, hospitals, banks, ports, parking lots, parks, stadiums, malls, train stations, airports, et cetera. The challenge is not for acquiring surveillance data from these video cameras, but for identifying what is valuable, what can be ignored, and what demands immediate attention. Computer vision systems attempt to construct meaningful and explicit descriptions of the environment or scene captured in an image. A few Computer Vision based security applications are presented here for securing building facility, railroad (Objects on railroad, and red signal detection), and roads.

Computer vision learning techniques for sports video analytics: removing overlays

2020 ◽  
Author(s):  
Arthur C. Depoian ◽  
Lorenzo E. Jaques ◽  
Dong Xie ◽  
Colleen P. Bailey ◽  
Parthasarathy Guturu
Keyword(s):  
Computer Vision ◽  
Video Analytics ◽  
Sports Video ◽  
Learning Techniques

scholarly journals Reliable service chain orchestration for scalable data-intensive computing at infrastructure edges

2019 ◽  
Author(s):  
◽  
Dmitrii Yurievich Chemodanov
Keyword(s):  
Computer Vision ◽  
Data Collection ◽  
Shortest Path ◽  
State Of The Art ◽  
Cloud Infrastructure ◽  
Data Intensive Computing ◽  
Video Analytics ◽  
Service Chain ◽  
Data Intensive ◽  
Reliable Service

In the event of natural or man-made disasters, geospatial video analytics is valuable to provide situational awareness that can be extremely helpful for first responders. However, geospatial video analytics demands massive imagery/video data 'collection' from Internet-of-Things (IoT) and their seamless 'computation/consumption' within a geo-distributed (edge/core) cloud infrastructure in order to cater to user Quality of Experience (QoE) expectations. Thus, the edge computing needs to be designed with a reliable performance while interfacing with the core cloud to run computer vision algorithms. This is because infrastructure edges near locations generating imagery/video content are rarely equipped with high-performance computation capabilities. This thesis addresses challenges of interfacing edge and core cloud computing within the geo-distributed infrastructure as a novel 'function-centric computing' paradigm that brings new insights to computer vision, edge routing and network virtualization areas. Specifically, we detail the state-of-the-art techniques and illustrate our new/improved solution approaches based on function-centric computing for the two problems of: (i) high-throughput data collection from IoT devices at the wireless edge, and (ii) seamless data computation/consumption within the geo-distributed (edge/core) cloud infrastructure. To address (i), we present a novel deep learning-augmented geographic edge routing that relies on physical area knowledge obtained from satellite imagery. To address (ii), we describe a novel reliable service chain orchestration framework that builds upon microservices and utilizes a novel 'metapath composite variable' approach supported by a constrained-shortest path finder. Finally, we show both analytically and empirically, how our geographic routing, constrained shortest path finder and reliable service chain orchestration approaches that compose our function-centric computing framework are superior than many traditional and state-of-the-art techniques. As a result, we can significantly speedup (up to 4 times) data-intensive computing at infrastructure edges fostering effective disaster relief coordination to save lives.

scholarly journals ARTIFICIAL INTELLIGENCE AND COMPUTER VISION – A MATCH MADE IN HEAVEN?

2021 ◽  
Vol 82 (1) ◽  
Author(s):  
Weng Kin Lai ◽  
Tomas Maul ◽  
Iman Yi Liao ◽  
Kam Meng Goh
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Manufacturing Sector ◽  
High Volume ◽  
Agriculture Sector ◽  
John Mccarthy ◽  
Brick And Mortar ◽  
Human Operators ◽  
High Level ◽  
Short Time

After becoming independent in 1957, Malaysia continued as an agricultural country but quickly grew into a manufacturing nation in a relatively short time. Literally from nowhere, the manufacturing sector now commands more than 38% of the nation’s GDP overtaking the agriculture sector which commands just slightly above 7%. In addition to the multinational manufacturers who are mainly in the electrical and electronics sectors, there are also other smaller producers who produce for the rest of the world. Nevertheless in order to compete, they cannot just rely on manual labour whether local or foreign, to produce high volume and high quality goods at a competitive price. With intense competition, even the old way of making many products to satisfy the global appetite for good products from both the brick-and-mortar shops to your huge online shops is no longer adequate. Manual operations in the manufacturing process can come in various forms, ranging from the very simple but monotonous and repetitive to the highly complex or sophisticated. In the quality department many of the local manufacturers have chosen to use human labour to ensure their quality is maintained. For many of these highly repetitive but relatively simple tasks, the human operators need to be properly trained for an appropriate length of time before they can perform effectively. Other than the intelligence of these operators, their ability to detect deviations from the desired patterns are also utilised. And this is where artificial intelligence and computer vision can help. The term artificial intelligence was first coined at the Dartmouth Summer Research Project on Artificial Intelligence by John McCarthy in 1956. While there are many definitions, Ray Kurzweil, an American inventor and futurist defines it as machines that perform functions that require intelligence when performed by humans[1]. On the other hand, computer vision deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. This paper shows how artificial intelligence combined with computer vision can be used to improve productivity and effectiveness in three different areas.

scholarly journals CONTOUR METHOD FOR POSITIONING OBJECTS IN MOBILE COMPUTER VISION SYSTEMS

2021 ◽  
pp. 62-69
Author(s):  
Sergey Kondratyev ◽  
Vitaliy Kostenko ◽  
Marina Yadrova
Keyword(s):  
Computer Vision ◽  
Control Unit ◽  
Contour Method ◽  
Raspberry Pi ◽  
Closed Contour ◽  
Vision Systems ◽  
Mobile Computer ◽  
Video Cameras ◽  
Computer Vision Systems

The paper considers the possibility of solving the problem of improving the quality of technical vision using the contour method, which is used to position objects in mobile computer vision systems. The hardware part of the object positioning system includes two video cameras, a Raspberry Pi 3 microcomputer, a depth contour map screen, and a motor control unit. The codes of programs based on the OpenCV library, the algorithm of the system and examples of the implementation of the contour method are given. The algorithm of the developed positioning technique includes the selection of the contours of objects on the frames of a stereopair, removal of all open contours, calculation of the moment (center of mass) of each closed contour, determination of the displacement along the x-axis of the moments of the corresponding contours, filling each closed contour with points with a brightness inversely proportional to the displacement of the moments. The presence of two video cameras, a Raspberry Pi 3 microcomputer, a contour depth map screen provides stereoscopic and panoramic "vision", that is, the ability to determine the presence of objects and their distance, as well as to get an overall picture in the "field of view" of the system. The engine control unit allows mobile devices to avoid obstacles. Based on the analysis of the research results, it was found that the proposed system provides an increase in the quality of positioning of objects and a decrease in the required computing resource, which gives a significant decrease in power consumption and ensures the autonomy of the system.

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