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Proceedings ArticleDOI

Low cost real-time system monitoring using Raspberry Pi

07 Jul 2015-pp 857-859
TL;DR: The design and implementation of a low-cost system monitoring based on Raspberry Pi, a single board computer which follows Motion Detection algorithm written in Python as a default programming environment, to significantly decrease storage usage and save investment costs are described.
Abstract: Nowadays, the Closed-Circuit Television (CCTV) surveillance system is being utilized in order to keep peace and provide security to people. There are several defects in the video surveillance system, such as: picture is indistinct, anomalies cannot be identified automatically, a lot of storage spaces are needed to save the surveillance information, and prices remain relatively high. This paper describes the design and implementation of a low-cost system monitoring based on Raspberry Pi, a single board computer which follows Motion Detection algorithm written in Python as a default programming environment. In addition, the system uses the motion detection algorithm to significantly decrease storage usage and save investment costs. The algorithm for motion detection is being implemented on Raspberry Pi, which enables live streaming camera along with detection of motion. The live video camera can be viewed from any web browser, even from mobile in real-time.
Citations
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Journal ArticleDOI
TL;DR: A smart home automation system using object detection algorithm based on model view controller (MVC) architecture deployed on Cloud of Things (CoT) architecture and the concept of distributed broker to support high number of publishers and subscribers is proposed.
Abstract: Object detection and recognition is commonly used in diverse computer vision based applications and many algorithms are proposed in literature. However, application of object detection algorithms in real-time systems demand minimal computation time and comprehensive performance analysis needs to be conducted before deployment. This paper aims to conduct performance analysis of deep learning based algorithm i.e. single shot detector (SSD) in IoT based embedded devices for smart home appliances control. We have developed a smart home automation system using object detection algorithm based on model view controller (MVC) architecture deployed on Cloud of Things (CoT) i.e. Amazon Web Service (AWS) cloud for users to remotely monitor their homes. Message queuing telemetry transport (MQTT) protocol is used for communication with connected IoT devices. For load-balancing, we have proposed the concept of distributed broker to support high number of publishers and subscribers. For experimental analysis, we have connected a camera with Raspberry Pi for object detection based on deep learning algorithm (SSD) using OpenCV library in our proposed system. Experiments are conducted to evaluate the performance of object detection algorithm under varying environmental conditions by changing the light intensity level, distance of object from camera, and frame size of video. Results show that communication delay is very low (i.e. 0.2 s) as compared to processing delay in Raspberry Pi. Furthermore, changing environmental conditions have very low/insignificant impact on the processing delay of object detection algorithm i.e. average delay of 1.7 s (stdev. 0.18) and 1.8 s (stdev. 0.24) in bright and dark lighting levels, respectively. However, accuracy is deteriorated under low lighting intensity level and increased frame sizes i.e. from 95–100 to 80–85%. Selected embedded device, camera model and object detection algorithm limits the performance of object detection in real-time systems and shall be carefully selected to fulfill the requirement.

44 citations

Journal ArticleDOI
TL;DR: The main contributions are the theoretical and experimental study to determine the aquarium background color and the algorithm of the proposed method implemented in a low cost and high performance embedded system, thus, allowing the counting instrument to be reliable, portable and easily migratory to different operating systems.

35 citations

Journal ArticleDOI
TL;DR: The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of the authors' digital society, and a large part of the data communicated is expected to consist ...
Abstract: The Internet of Things is predicted to consist of over 50 billion devices aiming to solve problems in most areas of our digital society. A large part of the data communicated is expected to consist ...

31 citations

Journal ArticleDOI
TL;DR: The construction of a low-cost, open-source mechanical ventilator for treating COVID-19 patients and a numerical method for monitoring the patients’ pulmonary condition, which alerts clinicians in real-time whether the patient is under a healthy or unhealthy situation.
Abstract: This paper shows the construction of a low-cost, open-source mechanical ventilator. The motivation for constructing this kind of ventilator comes from the worldwide shortage of mechanical ventilators for treating COVID-19 patients—the COVID-19 pandemic has been striking hard in some regions, especially the deprived ones. Constructing a low-cost, open-source mechanical ventilator aims to mitigate the effects of this shortage on those regions. The equipment documented here employs commercial spare parts only. This paper also shows a numerical method for monitoring the patients’ pulmonary condition. The method considers pressure measurements from the inspiratory limb and alerts clinicians in real-time whether the patient is under a healthy or unhealthy situation. Experiments carried out in the laboratory that had emulated healthy and unhealthy patients illustrate the potential benefits of the derived mechanical ventilator.

24 citations

Journal ArticleDOI
30 Mar 2020
TL;DR: The method put forward in the paper incorporates the motion sensors and the face identification system to detect the suspicious activities and report to the lawful person.
Abstract: The latest progress in the technology has led to automation and digitization in almost every fields, and has influenced a wide scope of application. This has caused enormous amount of data flow from each sectors, where the information contained in the data acts as the important component for the progress of the single person, organization, state, country and so on. These data with valuable information can be used in the constructive and the destructive perceptive based on the hands that handle it. So protective measures become very essential for preserving the data from unwanted access. This paves for developing a system to identify the suspicious movement in the volatile areas like military regimes, hospitals and financial organizations to safe the data. The method put forward in the paper incorporates the motion sensors and the face identification system to detect the suspicious activities and report to the lawful person. The algorithm for the system was developed using the python and tested for various sets of exemplary real time video recordings to know the accuracy in the detection.

21 citations

References
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Proceedings ArticleDOI
Claus Bahlmann1, Ying Zhu1, Visvanathan Ramesh1, M. Pellkofer2, T. Koehler2 
06 Jun 2005
TL;DR: This paper describes a computer vision based system for real-time robust traffic sign detection, tracking, and recognition that offers a generic, joint modeling of color and shape information without the need of tuning free parameters.
Abstract: This paper describes a computer vision based system for real-time robust traffic sign detection, tracking, and recognition. Such a framework is of major interest for driver assistance in an intelligent automotive cockpit environment. The proposed approach consists of two components. First, signs are detected using a set of Haar wavelet features obtained from AdaBoost training. Compared to previously published approaches, our solution offers a generic, joint modeling of color and shape information without the need of tuning free parameters. Once detected, objects are efficiently tracked within a temporal information propagation framework. Second, classification is performed using Bayesian generative modeling. Making use of the tracking information, hypotheses are fused over multiple frames. Experiments show high detection and recognition accuracy and a frame rate of approximately 10 frames per second on a standard PC.

447 citations

Proceedings ArticleDOI
01 Dec 2014
TL;DR: Raspberry Pi is used to investigate its applicative and cost effective use as a robotic arm in Object Sorting, which is Revolutionizing Robotic Systems in Industrial manufacturing plants by making them cheaper, compact & along with the same reliability as that of a dedicated PC.
Abstract: Usually sorting of objects is carried out manually using human labor. Recognizing a particular object and placing it in the required position is a tiring work especially in the field of industry where in one has to sort a bulk of objects in quick time and also the weight is greater than what a human can carry. This is when automation plays a major role. In this paper we are considering all these factors along with the cost to make the process more efficient. We use Raspberry Pi, which is an open source Linux based board. Raspberry Pi has found its way in major in number of useful & versatile applications in robotic systems. But never the less this system is new & hence Latest Technology takes time to be uncovered. Therefore, not much articles are available, hence our goal will be to investigate its applicative and cost effective use as a robotic arm in Object Sorting. Thereby Revolutionizing Robotic Systems in Industrial manufacturing plants by making them cheaper, compact & along with the same reliability as that of a dedicated PC. Furthermore we make use of a camera module which captures the image of the object. This image is processed using GNU Octave to determine the color and the shape of the object. GNU Octave is an open source language similar to MATLAB and hence making it portable. All the processed information in Octave is than relayed to a microcontroller which in turn controls the movement of the robotic arm which will segregate the objects into its respective compartments. Here we sort objects of three different shapes and colors, i.e. Square, Circle and Triangle (As seen from the Top-View of a Cube, Sphere/Cylinder and Triangular-Prism aligned vertically) and RGB colors i.e. Red, Blue and Green respectively.

30 citations


"Low cost real-time system monitorin..." refers methods in this paper

  • ...Instead of using the traditional wireless CCTV surveillance cameras, customers can now own their inexpensive security systems with the tiny super computer called Raspberry Pi [2]....

    [...]

Journal ArticleDOI
01 May 2014
TL;DR: A camera-based system combining video motion detection, motion estimation, and texture analysis with machine learning for sleep analysis is proposed and can give an indication of the PLMI score.
Abstract: In this paper, we propose a camera-based system combining video motion detection, motion estimation, and texture analysis with machine learning for sleep analysis. The system is robust to time-varying illumination conditions while using standard camera and infrared illumination hardware. We tested the system for periodic limb movement (PLM) detection during sleep, using EMG signals as a reference. We evaluated the motion detection performance both per frame and with respect to movement event classification relevant for PLM detection. The Matthews correlation coefficient improved by a factor of 2, compared to a state-of-the-art motion detection method, while sensitivity and specificity increased with 45% and 15%, respectively. Movement event classification improved by a factor of 6 and 3 in constant and highly varying lighting conditions, respectively. On 11 PLM patient test sequences, the proposed system achieved a 100% accurate PLM index (PLMI) score with a slight temporal misalignment of the starting time ( 1 s) regarding one movement. We conclude that camera-based PLM detection during sleep is feasible and can give an indication of the PLMI score.

27 citations


"Low cost real-time system monitorin..." refers background in this paper

  • ...Moreover, in recent years, Motion Detection [4], [5] has attracted a great interest from computer vision researchers due to its promising applications in many areas, such as video surveillance, traffic monitoring or sign language recognition....

    [...]

Journal Article
TL;DR: The problem of gesture recognition is narrowed down to that of hand gesture recognition and specifically deals with finger count extraction to facilitate further processing using the control so effected and a lucid albeit efficient algorithm is implemented using the embedded c System Generator software tool.
Abstract: Gesture recognition is a topic of immense interest in the field of computing and image processing involving numerous factors and constraints nevertheless yielding remarkable simplification of various human affairs. In this work, the problem of gesture recognition is narrowed down to that of hand gesture recognition and specifically deals with finger count extraction to facilitate further processing using the control so effected. A hand gesture recognition system has been developed, wherein the finger count in a certain input hand image is computed in accordance with a simple yet effective procedure. The problem of hand gesture recognition is solved by means of adopting a lucid albeit efficient algorithm which has been implemented using the embedded c System Generator software tool. The algorithm followed I invariant to rotation and scale of the hand. The approach involves segmenting the hand based on skin color statistics and applying certain constraints to classify pixels as skin and non skin regions. Those depending on the robotic arm move the direction and pick the object and drop the object using raspberry pi microcontroller. The Raspberry Pi is a credit card sized single computer or SoC uses ARM1176JZF-S core. SoC, or System on a Chip, is a method of placing all necessary electronics for running a computer on a single chip. Raspberry Pi needs an Operating system to start up. In the aim of cost reduction, the Raspberry Pi omits any on-board non-volatile memory used to store the boot loaders, Linux Kernels and file systems as seen in more traditional embedded systems. Rather, a SD/MMC card slot is provided for this purpose. After boot load, as per the application program Raspberry Pi will get execute.

10 citations


"Low cost real-time system monitorin..." refers background in this paper

  • ...There are several defects in the video surveillance system, such as: picture is indistinct, anomalies cannot be identified automatically, a lot of storage spaces are needed to save the surveillance information, and prices remain relatively high....

    [...]

Proceedings ArticleDOI
01 Feb 2014
TL;DR: A safety insurance approach is proposed, in which a user can inform his/her location for close observation via CCTV system if he/she feels a potential threat and a tracking system for mobile users based on CCTV system is studied.
Abstract: The Closed Circuit Television (CCTV) systems have been used at large scale for tracking and getting popularity with every passing day. The most common goal of CCTV system is prevention of crime and disorder by tracking the objects. The smartphone world is also expanding at a rapid pace since the technology was first introduced. Most users of smartphones live in those countries where usage of CCTV system is part of daily life. This paper studies a tracking system for mobile users based on CCTV system, where information can be sent from mobile to server so that CCTV system can work more specifically and accurately by tracking and recognizing objects. A safety insurance approach is proposed, in which a user can inform his/her location for close observation via CCTV system if he/she feels a potential threat. In case of emergency, location, nature of problem and possible difficulties can be determined in comparatively less time by authorities as they have already monitoring the situation.

8 citations


"Low cost real-time system monitorin..." refers background in this paper

  • ...Closed-circuit television monitoring system has now become an indispensable device in todays society [1]....

    [...]