A Motion Detection System in Python and Opencv

Parveen, Suraiya; Shah, Javeria

doi:10.1109/icicv50876.2021.9388404

Cited by 21 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It can quickly and accurately extract the foreground information of different types and eliminate noise. With both tracking and filtering in mind, a twoway matching method based on frame difference was designed to obtain the detected moving target, and a series of image filtering methods were combined in [16]. Bokeh plotting was used to detect and plot the time-frame in which the object was in front of the camera.…”

Section: F Edge Computingmentioning

confidence: 99%

Methods to Accelerate the Automation of CCTV Surveillance

Singaravelan¹,

J.²,

M.³

2022

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMM

View full text Add to dashboard Cite

As we evolve in the field of technology, the applications of the same in our day-to-day life have improved our lives. But this ease comes at a cost since, as the number of appliances used exponentially increases in our society, so does our demand for energy. This energy is usually obtained after destructive emissions are released into the environment, and the wastage of said energy can also be seen in several areas. A survey shows that a plugged-in mobile charger, when not used, consumes 0.1 to 0.5 watts per hour, costing ~15 rupees per day. Such wastage can be expected from other devices as well. Therefore, there is a scope for efficient energy management and, in recent times, it can be done more effectively using automation systems that eliminate the need for human interactions with devices or tools as much as possible. These systems may require the additional fitting of hardware such as sensors that will incur supplementary energy consumption and overhead costs for maintenance. They may also offer low-coverage for detection. Collaterally, it is worthwhile to note that the need for closed-circuit television (CCTV) installations is a basic necessity in several areas of the society and cannot be compromised to conserve energy and yet, we can utilize them to make a region of interest (ROI) energy efficient. This can be achieved through rationing the power consumption of other devices by inferring from an intelligible detection of objects and activities observed by a camera. It is made possible through a technique called Computer Vision (CV). CV, though reducing the workload of building setups for recognition, is a computationally exhaustive technique that requires hardware support to function with an accelerated performance for object detection in real-time. Thus, this work details the different methods available to detect objects and the techniques that can be employed to accelerate the performance of a low power consuming detection system. Graphics Processing Unit (GPU) acceleration and Edge computing are also discussed as a way to offer additional support to CV computation. The advantages and specific drawbacks of each method are also elaborated.

show abstract

Section: F Edge Computingmentioning

confidence: 99%

Methods to Accelerate the Automation of CCTV Surveillance

Singaravelan¹,

J.²,

M.³

2022

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMM

View full text Add to dashboard Cite

show abstract

“…By transforming, noise reduction, and noise reduction to reduce the useless information in the image, remove the noise in the image noise, and highlight data features for edge detection [11][12][13][14], feature extraction, image segmentation, and character recognition, the digital image processing aims to reduce the useless information in the image are used. In order to perform edge detection, feature extraction, image segmentation, and character recognition later, digital image processing reduces the image's unnecessary information, removes image noise, and highlights the features of the data image.…”

Section: Algorithm Implementationmentioning

confidence: 99%

Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Yan

Wanting

Liu

et al. 2022

MIST

View full text Add to dashboard Cite

This study proposed an intelligent algorithm based on digital image processing and character recognition to address the current situation in which the National Medical Products Administration promulgated the relevant regulations on the complete prohibition of the use of mercury sphygmomanometers in 2020 and the currently widely used electronic sphygmomanometers need to undergo regular verification and quality testing. The intelligent algorithm made it possible to automatically acquire the electronic sphygmomanometer indication value during quality assurance or verification. The images of the electronic sphygmomanometer were captured using a Raspberry Pi-connected video camera head; in the software development, automatic detection of the electronic sphygmomanometer's indication value was achieved by running the computer vision-based OpenCV library on the Raspberry Pi and utilizing image preprocessing techniques like scale transformation, grayscale conversion, Gaussian smoothing and edge detection, and character segmentation. The development of intelligent devices for the automatic verification of electronic sphygmomanometers has a technical foundation in the research and design of the digital recognition algorithm, and it has a certain reference value for character recognition of electronic instruments or the creation of automatic instrument indication value recording devices.

show abstract

“…However, the existing solutions rely on traditional methods, such as video compression [2], timed deletion [3], and manual intervention, none of which are efficient when it comes to the 21st century's technology standards. This paper presents an efficient solution for the storage and analysis of CCTV footage through motion-based frame removal [4]. The approach leverages OpenCV, a robust widely-used open-source computer vision library, to detect motion in the footage.…”

Section: Introductionmentioning

confidence: 99%

Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

Anderson

2023

Preprint

View full text Add to dashboard Cite

The storage and analysis of CCTV footage pose a significant challenge in modern surveillance systems. This paper presents an innovative and modern solution for efficient storage optimization and analysis through motion-based frame removal techniques. By leveraging the OpenCV library redundant frames are removed, resulting in substantial storage reduction without losing important motion events. The methodology uses Python’s OpenCV library with optional technologies such as Django, SQLite, and Bootstrap, ensuring reliability, scalability, usability, and effective data visualisation. Experimental results have demonstrated the efficacy across different camera setups in different environments and their ability to preserve critical information. The approach offers a practical solution for decreasing cost, improving analysis efficiency, and enhancing usability. This research contributes to the field of CCTV surveillance by providing a practical and industry-oriented solution. The findings inspire further improvements in storage optimization and pave the way for more intelligent surveillance systems.

show abstract

A Motion Detection System in Python and Opencv

Cited by 21 publications

References 7 publications

Methods to Accelerate the Automation of CCTV Surveillance

Methods to Accelerate the Automation of CCTV Surveillance

Studying electronic blood pressure monitor digital recognition algorithm based on computer vision and design

Efficient Storage and Analysis of Videos through Motion-Based Frame Removal

Contact Info

Product

Resources

About