Experimental Solution for Estimating Pedestrian Locations from UAV Imagery

Kainz, Ondrej; Gera, Marek; Michalko, M.; Jakab, F.

doi:10.3390/app12199485

Cited by 4 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the presented method, it is also possible to use semantic segmentation, which is currently a very active area of research [ 26 , 27 , 28 , 29 , 30 ]. There are many works on similar topics where semantic algorithms have been used to determine the areas.…”

Section: Discussionmentioning

confidence: 99%

Video Sequence Segmentation Based on K-Means in Air-Gap Data Transmission for a Cluttered Environment

Mazurek

Bak²

2023

Sensors

View full text Add to dashboard Cite

An air gap is a technique that increases the security of information systems. The use of unconventional communication channels allows for obtaining communication that is of interest to the attacker as well as to cybersecurity engineers. One of the very dangerous forms of attack is the use of computer screen brightness modulation, which is not visible to the user but can be observed from a distance by the attacker. Once infected, the computer can transmit data over long distances. Even in the absence of direct screen visibility, transmission can be realized by analyzing the modulated reflection of the monitor’s afterglow. The paper presents a new method for the automatic segmentation of video sequences to retrieve the transmitted data that does not have the drawbacks of the heretofore known method of growth (filling) based on an analysis of adjacent pixels. A fast camera operating at 380 fps was used for image acquisition. The method uses the characteristics of the amplitude spectrum for individual pixels, which is specific to the light sources in the room, and clustering with the k-means algorithm to group pixels into larger areas. Then, using the averaging of values for individual areas, it is possible to recover the 2-PAM (pulse-amplitude modulation) signal even at a 1000 times greater level of interference in the area to the transmitted signal, as shown in the experiments. The method does not require high-quality lenses.

show abstract

Section: Discussionmentioning

confidence: 99%

Video Sequence Segmentation Based on K-Means in Air-Gap Data Transmission for a Cluttered Environment

Mazurek

Bak²

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The YOLO algorithm is widely used for target detection in many different kinds of studies, such as estimating the positions of pedestrians from video recordings [48], identifying insect pests that influence agricultural production [49], iceberg and ship discrimination [50], ship detection and recognition in complex-scene SAR images [51], bridge detection [52], underwater object detection [53], automatic roadside feature detection [54], and object detection in automated driving [55].…”

Section: Yolo Algorithm For the Extraction Of Building Façade Element...mentioning

confidence: 99%

A Semi-Automated Two-Step Building Stock Monitoring Methodology for Supporting Immediate Solutions in Urban Issues

et al. 2023

View full text Add to dashboard Cite

The Sustainable Development Goals (SDGs) have addressed environmental and social issues in cities, such as insecure land tenure, climate change, and vulnerability to natural disasters. SDGs have motivated authorities to adopt urban land policies that support the quality and safety of urban life. Reliable, accurate, and up-to-date building information should be provided to develop effective land policies to solve the challenges of urbanization. Creating comprehensive and effective systems for land management in urban areas requires a significant long-term effort. However, some procedures should be undertaken immediately to mitigate the potential negative impacts of urban problems on human life. In developing countries, public records may not reflect the current status of buildings. Thus, implementing an automated and rapid building monitoring system using the potential of high-spatial-resolution satellite images and street views may be ideal for urban areas. This study proposed a two-step automated building stock monitoring mechanism. Our proposed method can identify critical building features, such as the building footprint and the number of floors. In the first step, buildings were automatically detected by using the object-based image analysis (OBIA) method on high-resolution spatial satellite images. In the second step, vertical images of the buildings were collected. Then, the number of the building floors was determined automatically using Google Street View Images (GSVI) via the YOLOv5 algorithm and the kernel density estimation method. The first step of the experiment was applied to the high-resolution images of the Pleiades satellite, which covers three different urban areas in Istanbul. The average accuracy metrics of the OBIA experiment for Area 1, Area 2, and Area 3 were 92.74%, 92.23%, and 92.92%, respectively. The second step of the experiment was applied to the image dataset containing the GSVIs of several buildings in different Istanbul streets. The perspective effect, the presence of more than one building in the photograph, some obstacles around the buildings, and different window sizes caused errors in the floor estimations. For this reason, the operator’s manual interpretation when obtaining SVIs increases the floor estimation accuracy. The proposed algorithm estimates the number of floors at a rate of 79.2% accuracy for the SVIs collected by operator interpretation. Consequently, our methodology can easily be used to monitor and document the critical features of the existing buildings. This approach can support an immediate emergency action plan to reduce the possible losses caused by urban problems. In addition, this method can be utilized to analyze the previous conditions after damage or losses occur.

show abstract