People detection in video streams using background subtraction and spatial-based scene modeling

Molina-Giraldo, S.; Insuasti-Ceballos, H. D.; Arroyave, Carlos M.; Montoya, Joseph; Lopez-Villa, J. S.; Álvarez-Meza, Andrés Marino; Castellanos-Domínguez, G.

doi:10.1109/stsiva.2015.7330394

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Detecting the moving objects is our major focus in this review, as for surveillance of human activities, the target is to automatically detect the foreground objects and to compute the required statistics. Various appealing foreground objects can be detected like, individuals [140], vehicles [141], baggage [136], planes [16], canoes [142] and products in warehouses [143]. Obviously, there are many examples of human activities environments and they are as follows:…”

Section: Human Activities Surveillancementioning

confidence: 99%

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

Yasir

Ali

2021

Int. J. Onl. Eng.

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In the computer vision, background extraction is a promising technique. It is characterized by being applied in many different real time applications in diverse environments and with variety of challenges. Background extraction is the most popular technique employed in the domain of detecting moving foreground objects taken by stationary surveillance cameras. Achieving high performance is required with many perspectives and demands. Choosing the suitable background extraction model plays the major role in affecting the performance matrices of time, memory, and accuracy.In this article we present an extensive review on background extraction in which we attempt to cover all the related topics. We list the four process stages of background extraction and we consider several well-known models starting with the conventional models and ending up with the state-of-the art models. This review also focuses on the model environments whether it is human activities, Nature or sport environments and illuminates on some of the real time applications where background extraction method is adopted. Many challenges are addressed in respect to environment, camera, foreground objects, background, and computation time. In addition, this article provides handy tables containing different common datasets and libraries used in the field of background extraction experiments. Eventually, we illustrate the performance evaluation with a table of the set performance metrics to measure the robustness of the background extraction model against other models in terms of time, accurate performance and required memory.

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Section: Human Activities Surveillancementioning

confidence: 99%

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

Yasir

Ali

2021

Int. J. Onl. Eng.

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“…To reduce the transaction time, many methods have been proposed [1], [2], [3]. Most of these methods degrade the qualities of the content data to complete analyses, such as resolutions for image data to reduce the data amount to collect.…”

Section: Introductionmentioning

confidence: 99%

A Method to Reduce Transaction Time for Real-time IoT Applications

Yukonhiatou

Yoshihisa

Kawakami

et al. 2019

Journal of Information Processing

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Due to the recent prevalence of IoT (Internet of Things) devices, streaming data such as video data or sensor data are collected and analyzed for real-time applications. The transaction time for data collection and analysis is one of the main factors to improve performance of some real-time IoT applications. For instance, in real-time surveillance camera systems, a shorter transaction time further reduces the delay to find thieves recorded by the cameras. However, computational and communication capacities of processing computers give lower limits to transaction time. To break this limitation, we propose an efficient data collection method using a progressive quality improvement approach. In our proposed method, each data source produces some content data of those qualities are lower than the quality of the original content data such as low resolution image data. Only the cases where higher quality data are needed for analyses, the processing computer progressively collects them. Thus, by reducing the average data amount of collections and for analyses, our proposed method reduces the average transaction time. We measured the transaction time of our proposed method in our developed simulator and confirmed that our proposed method can reduce the transaction time.

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Implementation of advanced authentication system using opencv by capturing motion images

Sharma

Dembla

Shekhar

2017

2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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People detection in video streams using background subtraction and spatial-based scene modeling

Cited by 5 publications

References 16 publications

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

Review on Real Time Background Extraction: Models, Applications, Environments, Challenges and Evaluation Approaches

A Method to Reduce Transaction Time for Real-time IoT Applications

Implementation of advanced authentication system using opencv by capturing motion images

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