Unmanned Aerial Vehicles for Crowd Monitoring and Analysis

Crowd monitoring and analysis has become increasingly used for unmanned aerial vehicle applications. From preventing stampede in high concentration crowds to estimating crowd density and to surveilling crowd movements, crowd monitoring and analysis have long been employed in the past by authorities and regulatory bodies to tackle challenges posed by large crowds. Conventional methods of crowd analysis using static cameras are limited due to their low coverage area and non-flexible perspectives and features. Unmanned aerial vehicles have tremendously increased the quality of images obtained for crowd analysis reasons, relieving the relevant authorities of the venues’ inadequacies and of concerns of inaccessible locations and situation. This paper reviews existing literature sources regarding the use of aerial vehicles for crowd monitoring and analysis purposes. Vehicle specifications, onboard sensors, power management, and an analysis algorithm are critically reviewed and discussed. In addition, ethical and privacy issues surrounding the use of this technology are presented.

Real Time Mask Detection, Social Distance and Crowd Analysis using Convolutional Neural Networks and YOLO Architecture Designs

Bu çalışmanın amacı koronavirüsün yayılım hızını düşürmede önemli bir etkisi olan maske takma, sosyal mesafe ve kalabılık analizinin yapılmasıdır. Bu analiz için çalışmada derin öğrenme tabanlı yöntemler olan Evrişimli Sinir Ağları (ESA) ve YOLO mimari tasarımları kullanılmıştır. Maske tespitinin yapılması için yeni bir veri seti oluşturulmuştur. Oluşturulan veri seti ‘maskeli’ ve ‘maskesiz’ sınıflandırma işleminin yapılması için AlexNet, DenseNet, MobileNet, ResNet, ShuffleNet, SqueezeNet, VGG, Xception ve ZFNet gibi ESA mimari tasarımları ile kullanılmıştır. En iyi sınıflandırma performansını %96.86 doğruluk oranı ve %91.81 F1-skoru değeri ile DenseNet-121 mimari tasarımı elde etmiştir. Sosyal mesafe ve kalabalık analizi için çalışmada YOLOv3 algoritması ve COCO veri seti kullanılmıştır. Daha sonra maske sınıflandırması görevinde elde edilen ağırlık dosyası, Haar Cascade yüz sınıflandırıcı algoritması ile birlikte kullanılarak, sosyal mesafe ve kalabalık analizini sağlayan algoritmaya dahil edilmiştir. Sonuç olarak ise hem maske tespitini sağlayan hem de sosyal mesafe ve kişi sayısını hesaplayan tümleşik bir yazılım oluşturulmuştur.

Hajj pilgrimage video analytics using CNN

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algorithm for applications in Hajj and Umrah. We create a new dataset based on the Hajj pilgrimage scenario in order to address this challenge. The proposed algorithm outperforms the state-of-the-art approach with a significant reduction of the mean absolute error (MAE) result: 240.0 (177.5 improvement) and the mean square error (MSE) result: 260.5 (280.1 improvement) when used with the latest dataset (HAJJ-Crowd dataset). We present density map and prediction of traditional approach in our novel HAJJ-crowd dataset for the purpose of evaluation with our proposed method.