Extra-thin infrared camera for low-cost surveillance applications

2014 ◽  
Vol 39 (11) ◽  
pp. 3169 ◽  
Author(s):  
Tatiana Grulois ◽  
Guillaume Druart ◽  
Nicolas Guérineau ◽  
Arnaud Crastes ◽  
Hervé Sauer ◽  
...  
Keyword(s):  
Low Cost ◽  
Infrared Camera ◽  
Surveillance Applications

scholarly journals Plant Leaf Detection and Counting in a Greenhouse during Day and Nighttime Using a Raspberry Pi NoIR Camera

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6659
Author(s):  
Aryuanto Soetedjo ◽  
Evy Hendriarianti
Keyword(s):  
Execution Time ◽  
Low Cost ◽  
Infrared Camera ◽  
Raspberry Pi ◽  
Camera System ◽  
Detection And Counting ◽  
Outdoor Environments ◽  
Static Images ◽  
Leaf Detection ◽  
Non Destructive

A non-destructive method using machine vision is an effective way to monitor plant growth. However, due to the lighting changes and complicated backgrounds in outdoor environments, this becomes a challenging task. In this paper, a low-cost camera system using an NoIR (no infrared filter) camera and a Raspberry Pi module is employed to detect and count the leaves of Ramie plants in a greenhouse. An infrared camera captures the images of leaves during the day and nighttime for a precise evaluation. The infrared images allow Otsu thresholding to be used for efficient leaf detection. A combination of numbers of thresholds is introduced to increase the detection performance. Two approaches, consisting of static images and image sequence methods are proposed. A watershed algorithm is then employed to separate the leaves of a plant. The experimental results show that the proposed leaf detection using static images achieves high recall, precision, and F1 score of 0.9310, 0.9053, and 0.9167, respectively, with an execution time of 551 ms. The strategy of using sequences of images increases the performances to 0.9619, 0.9505, and 0.9530, respectively, with an execution time of 516.30 ms. The proposed leaf counting achieves a difference in count (DiC) and absolute DiC (ABS_DiC) of 2.02 and 2.23, respectively, with an execution time of 545.41 ms. Moreover, the proposed method is evaluated using the benchmark image datasets, and shows that the foreground–background dice (FBD), DiC, and ABS_DIC are all within the average values of the existing techniques. The results suggest that the proposed system provides a promising method for real-time implementation.

Onboard Video Stabilization for Low Cost Small RPAS Surveillance Applications

2018 ◽  
Author(s):  
Ricardo Maroquio Bernardo ◽  
Luis Claudio Batista da Silva ◽  
Paulo Fernando Ferreira Rosa
Keyword(s):  
Low Cost ◽  
Video Stabilization ◽  
Surveillance Applications

Evaluation of an Uncooled Focal Plane Array Infrared Camera for Effusion Cooling Research

2000 ◽  
Author(s):  
Scott K. McGhee ◽  
A. M. Birk
Keyword(s):  
Image Analysis ◽  
Statistical Analysis ◽  
Focal Plane ◽  
Low Cost ◽  
Infrared Camera ◽  
Thermocouple Calibration ◽  
On Line ◽  
Research Grade ◽  
Temperature Maps ◽  
High Degree

This study assessed a low-cost, uncooled ferroelectric detector infrared camera for effusion cooling research. Advances in uncooled IR technology have led to applications previously limited to research-grade cameras. The imager operated in the 7–14μm waveband and sampled up to 30 frames per second. Thermal images were made of a matte-black flat plate, downstream of two cylindrical jets with injection angles of α = 30° and 90°, and L/D = 6. Thermocouple calibration was specific to each image. Statistical analysis and image analysis yielded detailed temperature maps with uncertainty as small as 0.9°C, a spatial resolution of 0.4mm, and a sensitivity of 0.1 °C. The system compared favorably with established infrared systems. Advantages include minimal instrumentation, on-line results, and a high degree of accuracy and resolution, at significantly reduced cost.

Low-cost low-power uncooled a-Si-based micro infrared camera for unattended ground sensor applications

1999 ◽  
Author(s):  
Thomas R. Schimert ◽  
David D. Ratcliff ◽  
John F. Brady III ◽  
Steven J. Ropson ◽  
Roland W. Gooch ◽  
...  
Keyword(s):  
Low Power ◽  
Low Cost ◽  
Infrared Camera ◽  
Sensor Applications

scholarly journals AutoTriage - An Open Source Edge Computing Raspberry Pi-based Clinical Screening System

2020 ◽  
Author(s):  
Chaitra Hegde ◽  
Zifan Jiang ◽  
Pradyumna Byappanahalli Suresha ◽  
Jacob Zelko ◽  
Salman Seyedi ◽  
...  
Keyword(s):  
Open Source ◽  
Low Cost ◽  
Characteristic Curve ◽  
Rapid Assessment ◽  
Infrared Camera ◽  
Visible Spectrum ◽  
Wait Time ◽  
Far Infrared ◽  
Raspberry Pi ◽  
Body Parts

AbstractWith the recent COVID-19 pandemic, healthcare systems all over the world are struggling to manage the massive increase in emergency department (ED) visits. This has put an enormous demand on medical professionals. Increased wait times in the ED increases the risk of infection transmission. In this work we present an open-source, low cost, off-body system to assist in the automatic triage of patients in the ED based on widely available hardware. The system initially focuses on two symptoms of the infection fever and cyanosis. The use of visible and far-infrared cameras allows for rapid assessment at a 1m distance, thus reducing the load on medical staff and lowering the risk of spreading the infection within hospitals. Its utility can be extended to a general clinical setting in non-emergency times as well to reduce wait time, channel the time and effort of healthcare professionals to more critical tasks and also prioritize severe cases.Our system consists of a Raspberry Pi 4, a Google Coral USB accelerator, a Raspberry Pi Camera v2 and a FLIR Lepton 3.5 Radiometry Long-Wave Infrared Camera with an associated IO module. Algorithms running in real-time detect the presence and body parts of individual(s) in view, and segments out the forehead and lip regions using PoseNet. The temperature of the forehead-eye area is estimated from the infrared camera image and cyanosis is assessed from the image of the lips in the visible spectrum. In our preliminary experiments, an accuracy of 97% was achieved for detecting fever and 77% for the detection of cyanosis, with a sensitivity of 91% and area under the receiver operating characteristic curve of 0.91. Heart rate and respiratory effort are also estimated from the visible camera.Although preliminary results are promising, we note that the entire system needs to be optimized before use and assessed for efficacy. The use of low-cost instrumentation will not produce temperature readings and identification of cyanosis that is acceptable in many situations. For this reason, we are releasing the full code stack and system design to allow others to rapidly iterate and improve the system. This may be of particular benefit in low-resource settings, and low-to-middle income countries in particular, which are just beginning to be affected by COVID-19.

Indexing Multi-Dimensional Trajectories for Similarity Queries

2011 ◽  
pp. 107-129 ◽  
Author(s):  
Michail Valachos ◽  
Marios Hadjieleftheriou ◽  
Eamonn Keogh ◽  
Dimitrios Gunopulos
Keyword(s):  
Time Series ◽  
Video Surveillance ◽  
Low Cost ◽  
Distance Measures ◽  
Similarity Queries ◽  
Indexing Scheme ◽  
Storage Devices ◽  
Surveillance Applications ◽  
Trajectory Indexing

With the abundance of low-cost storage devices, a plethora of applications that store and manage very large multi-dimensional trajectories (or time-series) datasets have emerged recently. Examples include traffic supervision systems, video surveillance applications, meteorology and more. Thus, it is becoming essential to provide a robust trajectory indexing framework designed especially for performing similarity queries in such applications. In this regard, this chapter presents an indexing scheme that can support a wide variety of (user-customizable) distance measures while, at the same time, it guarantees retrieval of similar trajectories with accuracy and efficiency.

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