Dense Crowds Detection and Surveillance with Drones using Density Maps

Detecting and Counting people in a human crowd from a moving drone present challenging problems that arisefrom the constant changing in the image perspective andcamera angle. In this paper, we test two different state-of-the-art approaches, density map generation with VGG19 trainedwith the Bayes loss function and detect-then-count with FasterRCNN with ResNet50-FPN as backbone, in order to comparetheir precision for counting and detecting people in differentreal scenarios taken from a drone flight. We show empiricallythat both proposed methodologies perform especially well fordetecting and counting people in sparse crowds when thedrone is near the ground. Nevertheless, VGG19 provides betterprecision on both tasks while also being lighter than FasterRCNN. Furthermore, VGG19 outperforms Faster RCNN whendealing with dense crowds, proving to be more robust toscale variations and strong occlusions, being more suitable forsurveillance applications using dronesComment: 2020 International Conference on Unmanned Aircraft Systems (ICUAS), Athens, Greece, 202

Enhancing drones for law enforcement and capacity monitoring at open large events

Police tasks related with law enforcement and citizen protection have gained a very useful asset in drones. Crowded demonstrations, large sporting events, or summer festivals are typical situations when aerial surveillance is necessary. The eyes in the sky are moving from the use of manned helicopters to drones due to costs, environmental impact, and discretion, resulting in local, regional, and national police forces possessing specific units equipped with drones. In this paper, we describe an artificial intelligence solution developed for the Castelldefels local police (Barcelona, Spain) to enhance the capabilities of drones used for the surveillance of large events. In particular, we propose a novel methodology for the efficient integration of deep learning algorithms in drone avionics. This integration improves the capabilities of the drone for tasks related with capacity control. These tasks have been very relevant during the pandemic and beyond. Controlling the number of persons in an open area is crucial when the expected crowd might exceed the capacity of the area and put humans in danger. The new methodology proposes an efficient and accurate execution of deep learning algorithms, which are usually highly demanding for computation resources. Results show that the state-of-the-art artificial intelligence models are too slow when utilised in the drone standard equipment. These models lose accuracy when images are taken at altitudes above 30 metres. With our new methodology, these two drawbacks can be overcome and results with good accuracy (96% correct segmentation and between 20% and 35% mean average proportional error) can be obtained in less than 20 s.This research was partially funded by the AGAUR research agency of Catalonia under grant number 2020PANDE00141 and by the Ministry of Science and Education of Spain under grant number PID2020-116377RB-C21.Peer ReviewedPostprint (published version

Deteksi Kerumunan Menggunakan Metode Fully-Convolutional Network pada Kamera Drone

Pada masa pandemi virus COVID-19 pemerintah menetapkan peraturan yang mengharuskan masyarakat untuk menerapkan beberapa protokol kesehatan. Salah satunya adalah menghindari kerumunan dan menjaga jarak. Untuk membantu pengawasan kepatuhan masyarakat terhadap protokol tersebut pada area yang luas, diperlukan sebuah sistem monitoring untuk memantau adanya kerumunan dengan menggunakan drone. Video yang direkam menggunakan kamera drone diproses menggunakan metode Fully-Convolutional Network (FCN) dengan menggabungkan loss function untuk tugas klasifikasi yang menentukan kerumunan atau tidak dan loss function untuk tugas regression yang menghitung kepadatan berdasarkan rata rata clustering coefficient. Penelitian ini mengimplementasikan metode FCN dengan input berupa rangkaian gambar yang diambil dari video sehingga menghasilkan output berupa keputusan apakah sejumlah orang dalam gambar itu berkerumun atau tidak. Data latih yang digunakan adalah VisDrone Dataset dan P-DESTRE Dataset yang terdiri dari rangkaian gambar yang direkam menggunakan drone yang diterbangkan dengan ketinggian rata-rata dengan mengambil contoh video berisi kerumunan dan bukan kerumunan. Hasil pengujian terbaik didapatkan menggunakan pre-trained model 5 dimana memiliki 2 keluaran yaitu 1 klasifikasi dan 1 regresi yaitu memiliki akurasi klasifikasi sebesar 0,978 sedangkan mean ablosute error untuk regresinya sebesar 0,141

Crowd Detection in Aerial Images Using Spatial Graphs and Fully-Convolutional Neural Networks

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Real-time Aerial Detection and Reasoning on Embedded-UAVs

We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multi-activities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model's accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open-field environment.Comment: In TGR

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed