Background modelling, analysis and implementation for thermographic images

Shallari, Irida; Anwar, Qaiser; Imran, Muhammad; O’Nils, Mattias

doi:10.1109/ipta.2017.8310078

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The people counting scenario relies on a set of image processing tasks starting with background modelling and subtraction as described in [31]. After subtraction we obtain the foreground image, a greyscale frame, which is later segmented based on a global predefined threshold.…”

Section: Methodsmentioning

confidence: 99%

From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications

Shallari

O’Nils

2019

Sensors

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The Internet of Things has grown quickly in the last few years, with a variety of sensing, processing and storage devices interconnected, resulting in high data traffic. While some sensors such as temperature, or humidity sensors produce a few bits of data periodically, imaging sensors output data in the range of megabytes every second. This raises a complexity for battery operated smart cameras, as they would be required to perform intensive image processing operations on large volumes of data, within energy consumption constraints. By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node, the fog computing layer and cloud computing, in the node energy consumption as well as the real time performance of the WVSN (Wireless Vision Sensor Node). The results obtained show that traditional design space exploration approaches are inefficient for WVSN, while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints.

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Section: Methodsmentioning

confidence: 99%

From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications

Shallari

O’Nils

2019

Sensors

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“…Background removal challenges include slow transition in image brightness, a sudden change in image illumination, shadows, and dynamic backgrounds [39]. By comparing different methods for removing backgrounds, it can be seen that removing backgrounds with KNN and removing image shadows for real-world data work better under a variety of lighting conditions than other methods [40]. We generate a binary video for each data, in which the video's background is black, and the moving subject is white.…”

Section: Proposed Approachmentioning

confidence: 99%

Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System

Darafsh,

Ghidary,

Zamani

2021

Preprint

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With the rapid increase in digital technologies, most fields of study include recognition of human activity and intention recognition, which are essential in smart environments. In this study, we equipped the activity recognition system with the ability to recognize intentions by affecting the pace of movement of individuals in the representation of images. Using this technology in various environments such as elevators and automatic doors will lead to identifying those who intend to pass the automatic door from those who are passing by. This system, if applied in elevators and automatic doors, will save energy and increase efficiency. For this study, data preparation is applied to combine the spatial and temporal features with the help of digital image processing principles. Nevertheless, unlike previous studies, only one AlexNet neural network is used instead of two-stream convolutional neural networks. Our embedded system was implemented with an accuracy of 98.78% on our intention recognition dataset. We also examined our data representation approach on other datasets, including HMDB-51, KTH, and Weizmann, and obtained accuracy of 78.48%, 97.95%, and 100%, respectively. The image recognition and neural network models were simulated and implemented using Xilinx simulators for the Xilinx ZCU102 board. The operating frequency of this embedded system is 333 MHz, and it works in real-time with 120 frames per second (fps).

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“…To overcome these complexities, we used a low-resolution thermal sensor that gives a generic temperature profile of the region/object of interest while providing a low-weight system compared to RGB cameras [45]. For the people counting scenario, we created our own dataset from a setup installed in Härnösand, Sweden.…”

Section: People Countingmentioning

confidence: 99%

Communication and computation inter-effects in people counting using intelligence partitioning

Shallari

Krug

O’Nils

2020

J Real-Time Image Proc

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The rapid development of the Internet of Things is affecting the requirements towards wireless vision sensor networks (WVSN). Future smart camera architectures require battery-operated devices to facilitate deployment for scenarios such as industrial monitoring, environmental monitoring and smart city, consequently imposing constraints on the node energy consumption. This paper provides an analysis of the inter-effects between computation and communication energy for a smart camera node. Based on a people counting scenario, we evaluate the trade-off for the node energy consumption with different processing configurations of the image processing tasks, and several communication technologies. The results indicate that the optimal partition between the smart camera node and remote processing is with background modelling, segmentation, morphology and binary compression implemented in the smart camera, supported by Bluetooth Low Energy (BLE) version 5 technologies. The comparative assessment of these results with other implementation scenarios underlines the energy efficiency of this approach. This work changes pre-conceptions regarding design space exploration in WVSN, motivating further investigation regarding the inclusion of intermediate processing layers between the node and the cloud to interlace low-power configurations of communication and processing architectures.

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Background modelling, analysis and implementation for thermographic images

Cited by 3 publications

References 16 publications

From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications

From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications

Real-Time Activity Recognition and Intention Recognition Using a Vision-based Embedded System

Communication and computation inter-effects in people counting using intelligence partitioning

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