Unsupervised automatic tracking of thermal changes in human body

An automated system for detecting and tracking of the thermal fluctuation in human body is addressed. It applies HSV based k-means clustering which initialized and controlled the points which lie on the ROI boundary. Afterward a particle filter tracked the targeted ROI in the thermal video stream. There were six subjects have voluntarily participated on these experiments. For simulating the hot spots occur during the some medical tests a controllable heater utilized close to the subjects body. The results indicated promising accuracy of the proposed approach for tracking the hot spots. However, there were some approximations (e.g. the transmittance of the atmosphere and emissivity of the fabric) which can be neglected because of independency of the proposed approach for these parameters. The approach can track the heating spots efficiently considering the movement in the subjects which provided a confidence of considerable robustness against motion-artifact usually occurs in the medical tests

The role of seas as a geographical barrier for migratory landbirds. An approach to the Bay of Biscay.

131 p.The East-Atlantic flyway represents one of the main bird migration routes worldwide, comprising the Bay of Biscayas an inherent geographical barrier. So far, the significance of the Bay of Biscay for migrants and its potentialimpact on migratory routes have not received much attention in research. The general aim of this thesis is to unravelthe role of the Bay of Biscay as a geographic barrier both at a small and large scale shaping the nocturnalmigration of landbirds moving along the East-Atlantic flyway. All observation tools available in the study regiondeemed suitable for nocturnal studies were employed: operational wind profiler and weather radars, thermalimaging and moonwatching. Previous knowledge from visual observations and bird ringing available in literaturewas also taken into account. Furthermore, in a pioneer approach, the study assessed the potential use ofwind profiler data in an ornithological context. An objective qualitative and quantitative approach validated bythermal imaging was established to extract migration parameters.Migration traffic rates, flight directions and altitudes obtained by the two types of radars and thermal imagingfor various sites along the bay are provided and discussed in a meteorological and ecological context. Verticalhistorical wind profiler and current thermal imaging data indicated pronounced broad-front migration in earlyspring vs. more eastward (i.e. sea-avoidance) migration in autumn. Moonwatching provided additional informationon bird composition and confirmed predominance of passerines. Finally, horizontal weather radar datarevealed a north-south gradient along the French coast in spring, with higher intensity in the south-easternstudy area close to the main migration axis of the East-Atlantic flyway.Aranzadi Zientzia Elkarte

무인비행체 탑재 열화상 및 실화상 이미지를 활용한 야생동물 탐지 가능성 연구

학위논문(석사) -- 서울대학교대학원 : 환경대학원 환경조경학과, 2022.2. 송영근.야생동물의 탐지와 모니터링을 위해, 현장 직접 관찰, 포획-재포획과 같은 전통적 조사 방법이 다양한 목적으로 수행되어왔다. 하지만, 이러한 방법들은 많은 시간과 상대적으로 비싼 비용이 필요하며, 신뢰 가능한 탐지 결과를 얻기 위해선 숙련된 현장 전문가가 필요하다. 게다가, 전통적인 현장 조사 방법은 현장에서 야생동물을 마주치는 등 위험한 상황에 처할 수 있다. 이에 따라, 카메라 트래핑, GPS 추적, eDNA 샘플링과 같은 원격 조사 방법이 기존의 전통적 조사방법을 대체하며 더욱 빈번히 사용되기 시작했다. 하지만, 이러한 방법들은 여전히 목표로 하는 대상의 전체 면적과, 개별 개체를 탐지할 수 없다는 한계를 가지고 있다. 이러한 한계를 극복하기 위해, 무인비행체 (UAV, Unmanned Aerial Vehicle)가 야생동물 탐지의 대중적인 도구로 자리매김하고 있다. UAV의 가장 큰 장점은, 선명하고 촘촘한 공간 및 시간해상도와 함께 전체 연구 지역에 대한 동물 탐지가 가능하다는 것이다. 이에 더해, UAV를 사용함으로써, 접근하기 어려운 지역이나 위험한 곳에 대한 조사가 가능해진다. 하지만, 이러한 이점 외에, UAV의 단점도 명확히 존재한다. 대상지, 비행 속도 및 높이 등과 같이 UAV를 사용하는 환경에 따라, 작은 동물, 울창한 숲속에 있는 개체, 빠르게 움직이는 동물을 탐지하는 것이 제한된다. 또한, 기상환경에 따라서도 비행이 불가할 수 있고, 배터리 용량으로 인한 비행시간의 제한도 존재한다. 하지만, 정밀한 탐지가 불가능하더라도, 이와 관련 연구가 꾸준히 수행되고 있으며, 선행연구들은 육상 및 해상 포유류, 조류, 그리고 파충류 등을 탐지하는 데에 성공하였다. UAV를 통해 얻어지는 가장 대표적인 데이터는 실화상 이미지이다. 이를 사용해 머신러닝 및 딥러닝 (ML-DL, Machine Learning and Deep Learning) 방법이 주로 사용되고 있다. 이러한 방법은 상대적으로 정확한 탐지 결과를 보여주지만, 특정 종을 탐지할 수 있는 모델의 개발을 위해선 최소한 천 장의 이미지가 필요하다. 실화상 이미지 외에도, 열화상 이미지 또한 UAV를 통해 획득 될 수 있다. 열화상 센서 기술의 개발과 센서 가격의 하락은 많은 야생동물 연구자들의 관심을 사로잡았다. 열화상 카메라를 사용하면 동물의 체온과 주변환경과의 온도 차이를 통해 정온동물을 탐지하는 것이 가능하다. 하지만, 새로운 데이터가 사용되더라도, 여전히 ML-DL 방법이 동물 탐지에 주로 사용되고 있으며, 이러한 방법은 UAV를 활용한 야생동물의 실시간 탐지를 제한한다. 따라서, 본 연구는 열화상과 실화상 이미지를 활용한 동물 자동 탐지 방법의 개발과, 개발된 방법이 이전 방법들의 평균 이상의 정확도와 함께 현장에서 실시간으로 사용될 수 있도록 하는 것을 목표로 한다.For wildlife detection and monitoring, traditional methods such as direct observation and capture-recapture have been carried out for diverse purposes. However, these methods require a large amount of time, considerable expense, and field-skilled experts to obtain reliable results. Furthermore, performing a traditional field survey can result in dangerous situations, such as an encounter with wild animals. Remote monitoring methods, such as those based on camera trapping, GPS collars, and environmental DNA sampling, have been used more frequently, mostly replacing traditional survey methods, as the technologies have developed. But these methods still have limitations, such as the lack of ability to cover an entire region or detect individual targets. To overcome those limitations, the unmanned aerial vehicle (UAV) is becoming a popular tool for conducting a wildlife census. The main benefits of UAVs are able to detect animals remotely covering a wider region with clear and fine spatial and temporal resolutions. In addition, by operating UAVs investigate hard to access or dangerous areas become possible. However, besides these advantages, the limitations of UAVs clearly exist. By UAV operating environments such as study site, flying height or speed, the ability to detect small animals, targets in the dense forest, tracking fast-moving animals can be limited. And by the weather, operating UAV is unable, and the flight time is limited by the battery matters. Although detailed detection is unavailable, related researches are developing and previous studies used UAV to detect terrestrial and marine mammals, avian and reptile species. The most common type of data acquired by UAVs is RGB images. Using these images, machine-learning and deep-learning (ML–DL) methods were mainly used for wildlife detection. ML–DL methods provide relatively accurate results, but at least 1,000 images are required to develop a proper detection model for specific species. Instead of RGB images, thermal images can be acquired by a UAV. The development of thermal sensor technology and sensor price reduction has attracted the interest of wildlife researchers. Using a thermal camera, homeothermic animals can be detected based on the temperature difference between their bodies and the surrounding environment. Although the technology and data are new, the same ML–DL methods were typically used for animal detection. These ML-DL methods limit the use of UAVs for real-time wildlife detection in the field. Therefore, this paper aims to develop an automated animal detection method with thermal and RGB image datasets and to utilize it under in situ conditions in real-time while ensuring the average-above detection ability of previous methods.Abstract I Contents IV List of Tables VII List of Figures VIII Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research goals and objectives 10 1.2.1 Research goals 10 1.2.2 Research objectives 11 1.3 Theoretical background 13 1.3.1 Concept of the UAV 13 1.3.2 Concept of the thermal camera 13 Chapter 2. Methods 15 2.1 Study site 15 2.2 Data acquisition and preprocessing 16 2.2.1 Data acquisition 16 2.2.2 RGB lens distortion correction and clipping 19 2.2.3 Thermal image correction by fur color 21 2.2.4 Unnatural object removal 22 2.3 Animal detection 24 2.3.1 Sobel edge creation and contour generation 24 2.3.2 Object detection and sorting 26 Chapter 3. Results 30 3.1 Number of counted objects 31 3.2 Time costs of image types 33 Chapter 4. Discussion 36 4.1 Reference comparison 36 4.2 Instant detection 40 4.3 Supplemental usage 41 4.4 Utility of thermal sensors 42 4.5 Applications in other fields 43 Chapter 5. Conclusions 47 References 49 Appendix: Glossary 61 초록 62석

Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation

This thesis was submitted for the degree of Engineering Doctorate and awarded by Brunel University.Birds are a key environmental asset and this is recognised through comprehensive legislation and policy ensuring their protection and conservation. Many species are active at night and surveys are required to understand the implications of proposed developments such as towers and reduce possible conflicts with these structures. Night vision devices are commonly used in nocturnal surveys, either to scope an area for bird numbers and activity, or in remotely sensing an area to determine potential risk. This thesis explores some practical and theoretical approaches that can improve the accuracy, confidence and efficiency of nocturnal bird surveillance. As image intensifiers and thermal imagers have operational differences, each device has associated strengths and limitations. Empirical work established that image intensifiers are best used for species identification of birds against the ground or vegetation. Thermal imagers perform best in detection tasks and monitoring bird airspace usage. The typically used approach of viewing bird survey video from remote sensing in its entirety is a slow, inaccurate and inefficient approach. Accuracy can be significantly improved by viewing the survey video at half the playback speed. Motion detection efficiency and accuracy can be greatly improved through the use of adaptive background subtraction and cumulative image differencing. An experienced ornithologist uses bird flight style and wing oscillations to identify bird species. Changes in wing oscillations can be represented in a single inter-frame similarity matrix through area-based differencing. Bird species classification can then be automated using singular value decomposition to reduce the matrices to one-dimensional vectors for training a feed-forward neural network

Webcams for Bird Detection and Monitoring: A Demonstration Study

Better insights into bird migration can be a tool for assessing the spread of avian borne infections or ecological/climatologic issues reflected in deviating migration patterns. This paper evaluates whether low budget permanent cameras such as webcams can offer a valuable contribution to the reporting of migratory birds. An experimental design was set up to study the detection capability using objects of different size, color and velocity. The results of the experiment revealed the minimum size, maximum velocity and contrast of the objects required for detection by a standard webcam. Furthermore, a modular processing scheme was proposed to track and follow migratory birds in webcam recordings. Techniques such as motion detection by background subtraction, stereo vision and lens distortion were combined to form the foundation of the bird tracking algorithm. Additional research to integrate webcam networks, however, is needed and future research should enforce the potential of the processing scheme by exploring and testing alternatives of each individual module or processing step

Incremental low rank noise reduction for robust infrared tracking of body temperature during medical imaging

Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performanc

Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks

The availability of Internet, line-of-sight and satellite identification and surveillance information as well as low-power, low-cost embedded systems-on-a-chip and a wide range of visible to long-wave infrared cameras prompted Embry Riddle Aeronautical University to collaborate with the University of Alaska Arctic Domain Awareness Center (ADAC) in summer 2016 to prototype a camera system we call the SDMSI (Software-Defined Multi-spectral Imager). The concept for the camera system from the start has been to build a sensor node that is drop-in-place for simple roof, marine, pole-mount, or buoy-mounts. After several years of component testing, the integrated SDMSI is now being tested, first on a roof-mount at Embry Riddle Prescott. The roof-mount testing demonstrates simple installation for the high spatial, temporal and spectral resolution SDMSI. The goal is to define and develop software and systems technology to complement satellite remote sensing and human monitoring of key resources such as drones, aircraft and marine vessels in and around airports, roadways, marine ports and other critical infrastructure. The SDMSI was installed at Embry Riddle Prescott in fall 2016 and continuous recording of long-wave infrared and visible images have been assessed manually and compared to salient object detection to automatically record only frames containing objects of interest (e.g. aircraft and drones). It is imagined that ultimately users of the SDMSI can pair with it via wireless to browse salient images. Further, both ADS-B (Automatic Dependent Surveillance-Broadcast) and S-AIS (Satellite Automatic Identification System) data are envisioned to be used by the SDMSI to form expectations for observing in future tests. This paper presents the preliminary results of several experiments and compares human review with smart image processing in terms of the receiver-operator characteristic. The system design and software are open architecture, such that other researchers are encouraged to construct and participate in sharing results and networking identical or improved versions of the SDMSI for safety, security and drop-in-place scientific image sensor networking

Automated assessment and tracking of human body thermal variations using unsupervised clustering

The presented approach addresses a review of the overheating that occurs during radiological examinations, such as magnetic resonance imaging, and a series of thermal experiments to determine a thermally suitable fabric material that should be used for radiological gowns. Moreover, an automatic system for detecting and tracking of the thermal fluctuation is presented. It applies hue-saturated-value-based kernelled k-means clustering, which initializes and controls the points that lie on the region-of-interest (ROI) boundary. Afterward, a particle filter tracks the targeted ROI during the video sequence independently of previous locations of overheating spots. The proposed approach was tested during experiments and under conditions very similar to those used during real radiology exams. Six subjects have voluntarily participated in these experiments. To simulate the hot spots occurring during radiology, a controllable heat source was utilized near the subject’s body. The results indicate promising accuracy for the proposed approach to track hot spots. Some approximations were used regarding the transmittance of the atmosphere, and emissivity of the fabric could be neglected because of the independence of the proposed approach for these parameters. The approach can track the heating spots continuously and correctly, even for moving subjects, and provides considerable robustness against motion artifact, which occurs during most medical radiology procedures