Post-disaster rescue facility: Human detection and geolocation using aerial drones

Rivera, Alan; Villalobos, A.D.C.; Monje, Jose Claro; Mariñas, Juan Antonio G.; Oppus, Carlos

doi:10.1109/tencon.2016.7848026

Cited by 37 publications

(16 citation statements)

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“…The thermal camera was used to prefilter promising image locations and subsequently a visual object detector was used to verify them. Rivera et al [25] presented a human detection and geolocation system using aerial drones to search for victims in SAR operations. They designed modular equipment by combining thermal and colour cameras and a geolocation module attached to a drone.…”

Section: Using Thermal and Ir Cameras For Human Detectionmentioning

confidence: 99%

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

et al. 2020

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In search and rescue operations, it is crucial to rapidly identify those people who are alive from those who are not. If this information is known, emergency teams can prioritize their operations to save more lives. However, in some natural disasters the people may be lying on the ground covered with dust, debris, or ashes making them difficult to detect by video analysis that is tuned to human shapes. We present a novel method to estimate the locations of people from aerial video using image and signal processing designed to detect breathing movements. We have shown that this method can successfully detect clearly visible people and people who are fully occluded by debris. First, the aerial videos were stabilized using the key points of adjacent image frames. Next, the stabilized video was decomposed into tile videos and the temporal frequency bands of interest were motion magnified while the other frequencies were suppressed. Image differencing and temporal filtering were performed on each tile video to detect potential breathing signals. Finally, the detected frequencies were remapped to the image frame creating a life signs map that indicates possible human locations. The proposed method was validated with both aerial and ground recorded videos in a controlled environment. Based on the dataset, the results showed good reliability for aerial videos and no errors for ground recorded videos where the average precision measures for aerial videos and ground recorded videos were 0.913 and 1 respectively.

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Section: Using Thermal and Ir Cameras For Human Detectionmentioning

confidence: 99%

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

et al. 2020

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“…Kang et al [31] proposed an approach for tracking continuously multiple objects across multiple sensors (i.e., electrooptical (EO) and infra-red (IR) sensors) using a joint probability model, which encodes the object's appearance and motion. Rivera et al [32] produced a modular equipment consisting of two cameras (i.e., thermal and color cameras) and a geolocation module mounted on a drone for possible human survivors in areas afflicted by disaster. Additionally, the data implied that optical detection effectively operates during daytime, having a higher accuracy at daytime deployment in comparison to nighttime.…”

Section: Sar Operations Using Thermal and Infrared Radiation (Ir) Cammentioning

confidence: 99%

Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

2021

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The locations and breathing signal of people in disaster areas are significant information for search and rescue missions in prioritizing operations to save more lives. For detecting the living people who are lying on the ground and covered with dust, debris or ashes, a motion magnification-based method has recently been proposed. This current method estimates the locations and breathing signal of people from a drone video by assuming that only human breathing-related motions exist in the video. However, in natural disasters, background motions, such as swing trees and grass caused by wind, are mixed with human breathing, that distort this assumption, resulting in misleading or even no life signs locations. Therefore, the life signs in disaster areas are challenging to be detected due to the undesired background motions. Note that human breathing is a natural physiological phenomenon, and it is a periodic motion with a steady peak frequency; while background motion always involves complex space-time behaviors, their peak frequencies seem to be variable over time. Therefore, in this work we analyze and focus on the frequency properties of motions to model a frequency variability feature used for extracting only human breathing, while eliminating irrelevant background motions in the video, which would ease the challenge in detection and localization of life signs. The proposed method was validated with both drone and camera videos recorded in the wild. The average precision measures of our method for drone and camera videos were 0.94 and 0.92, which are higher than that of compared methods, demonstrating that our method is more robust and accurate to background motions. The implications and limitations regarding the frequency variability feature were discussed.

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“…Human detection from the aerial platform is also gaining popularity. Both optical and thermal sensors are used in the detection process [1] [12][13][14][15]. Rudol & Doherty have utilized the UAV platform to detect human beings using thermal and optical sensor.…”

Section: Related Workmentioning

confidence: 99%

Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV

Bhattarai¹,

Nakamura²,

Mozumder³

2018

Preprint

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Human detection from Unmanned Aerial Vehicles (UAV) is gaining popularity in the field of disaster management, crowd counting, people monitoring. Real time human detection from UAV is a challenging task, because of many constraints involved. This study proposes a system for real time detection of humans on videos captured from UAVs addressing three of these constraints namely, flying height, computation time and scale of viewing. The proposed method integrated an android application with a binary classifier based on Haar-features to automatically detect human / non-human class from UAV images. The video frames were parsed and detected humans from image frames were geo-localized and visualized on Google Earth. The performance was evaluated for geo-localization accuracy, computation time and detection accuracy, considering human coverage – pixel size relationship for various heights and scale factor. Based on flying height - human size relationship and tradeoff between detection accuracy vs computation time, the study came up with optimal parameters for OpenCV’s cv2.cascadeClassifier. detectMultiScale function. This paper establishes a strong ground for further research relating to real time human detection from UAV.

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Post-disaster rescue facility: Human detection and geolocation using aerial drones

Cited by 37 publications

References 1 publication

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

Detection and Localisation of Life Signs from the Air Using Image Registration and Spatio-Temporal Filtering

Frequency Variability Feature for Life Signs Detection and Localization in Natural Disasters

Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV

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