Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies

2021 ◽  
Vol 70 ◽  
pp. 102908
Author(s):  
Imran Ahmed ◽  
Gwanggil Jeon ◽  
Abdellah Chehri ◽  
Mohammad Mehedi Hassan
Keyword(s):  
Transfer Learning ◽  
Smart Cities ◽  
Human Detection ◽  
Overhead View

scholarly journals Challenges and Limitations in Human Action Recognition on Unmanned Aerial Vehicles: A Comprehensive Survey

2021 ◽  
Vol 38 (5) ◽  
pp. 1403-1411
Author(s):  
Nashwan Adnan Othman ◽  
Ilhan Aydin
Keyword(s):  
Action Recognition ◽  
Smart Cities ◽  
Large Angle ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Detection ◽  
Long Distance ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Aerial Vehicle

An Unmanned Aerial Vehicle (UAV), commonly called a drone, is an aircraft without a human pilot aboard. Making UAVs that can accurately discover individuals on the ground is very important for various applications, such as people searches, and surveillance. UAV integration in smart cities is challenging, however, because of problems and concerns such as privacy, safety, and ethical/legal use. Human action recognition-based UAVs can utilize modern technologies. Thus, it is essential for future development of the aforementioned applications. UAV-based human activity recognition is the procedure of classifying photo sequences with action labels. This paper offers a comprehensive study of UAV-based human action recognition techniques. Furthermore, we conduct empirical research studies to assess several factors that might influence the efficiency of human detection and action recognition techniques in UAVs. Benchmark datasets commonly utilized for UAV-based human action recognition are briefly explained. Our findings reveal that the existing human action recognition innovations can identify human actions on UAVs with some limitations in range, altitudes, long-distance, and a large angle of depression.

scholarly journals Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm

2020 ◽  
Author(s):  
Halgurd S. Maghdid ◽  
Kayhan Zrar Ghafoor ◽  
Abdulbasit Al‐Talabani ◽  
Ali Safaa Sadiq ◽  
Pranav Kumar Singh ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Indoor Localization ◽  
Learning Algorithm ◽  
Smart Cities

scholarly journals Social Media Meets Big Urban Data: A Case Study of Urban Waterlogging Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ningyu Zhang ◽  
Huajun Chen ◽  
Jiaoyan Chen ◽  
Xi Chen
Keyword(s):  
Social Media ◽  
Transfer Learning ◽  
Traffic Management ◽  
Smart Cities ◽  
Learning Method ◽  
Small Cities ◽  
China And India ◽  
The Moment ◽  
Traffic Operation

With the design and development of smart cities, opportunities as well as challenges arise at the moment. For this purpose, lots of data need to be obtained. Nevertheless, circumstances vary in different cities due to the variant infrastructures and populations, which leads to the data sparsity. In this paper, we propose a transfer learning method for urban waterlogging disaster analysis, which provides the basis for traffic management agencies to generate proactive traffic operation strategies in order to alleviate congestion. Existing work on urban waterlogging mostly relies on past and current conditions, as well as sensors and cameras, while there may not be a sufficient number of sensors to cover the relevant areas of a city. To this end, it would be helpful if we could transfer waterlogging. We examine whether it is possible to use the copious amounts of information from social media and satellite data to improve urban waterlogging analysis. Moreover, we analyze the correlation between severity, road networks, terrain, and precipitation. Moreover, we use a multiview discriminant transfer learning method to transfer knowledge to small cities. Experimental results involving cities in China and India show that our proposed framework is effective.

scholarly journals Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities

2020 ◽  
Author(s):  
Ahmed Alghamdi ◽  
Mohamed Hammad ◽  
Hassan Ugail ◽  
Asmaa Abdel-Raheem ◽  
Khan Muhammad ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Transfer Learning ◽  
Smart Cities ◽  
Learning Methods

scholarly journals Monthly Electric Load Forecasting Using Transfer Learning for Smart Cities

2020 ◽  
Vol 12 (16) ◽  
pp. 6364 ◽  
Author(s):  
Seung-Min Jung ◽  
Sungwoo Park ◽  
Seung-Won Jung ◽  
Eenjun Hwang
Keyword(s):  
Transfer Learning ◽  
Smart Cities ◽  
Pearson Correlation ◽  
Load Forecasting ◽  
Machine Learning Techniques ◽  
Weather Data ◽  
Similar Data ◽  
Electric Load ◽  
Learning Techniques ◽  
Electric Load Forecasting

Monthly electric load forecasting is essential to efficiently operate urban power grids. Although diverse forecasting models based on artificial intelligence techniques have been proposed with good performance, they require sufficient datasets for training. In the case of monthly forecasting, because just one data point is generated per month, it is not easy to collect sufficient data to construct models. This lack of data can be alleviated using transfer learning techniques. In this paper, we propose a novel monthly electric load forecasting scheme for a city or district based on transfer learning using similar data from other cities or districts. To do this, we collected the monthly electric load data from 25 districts in Seoul for five categories and various external data, such as calendar, population, and weather data. Then, based on the available data of the target city or district, we selected similar data from the collected datasets by calculating the Pearson correlation coefficient and constructed a forecasting model using the selected data. Lastly, we fine-tuned the model using the target data. To demonstrate the effectiveness of our model, we conducted an extensive comparison with other popular machine-learning techniques through various experiments. We report some of the results.

scholarly journals Transfer learning-based method for automated e-waste recycling in smart cities

2021 ◽  
pp. 169337
Author(s):  
Nermeen Baker ◽  
Paul Szabo-Müller ◽  
Uwe Handmann
Keyword(s):  
Transfer Learning ◽  
Smart Cities ◽  
Waste Recycling
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