Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images

Castellano, Giovanna; Castiello, Ciro; Cianciotta, Marco; Mencar, Corrado; Vessio, Gennaro

doi:10.1007/978-3-030-66823-5_35

Cited by 13 publications

(9 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…b) Deep Learning: According to K. Van Beeck et al [102], in the 'UAVision2020' workshop which focused on realtime image processing on-board UAVs, all accepted workshop papers (covering a wide range of different applications) used deep learning. Castellano et al [108] and Zhao el al. [109] were presented as good examples of this use of deep learning where both described the use of CNNs for crowd counting or understanding.…”

Section: E Computer Visionmentioning

confidence: 95%

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

McEnroe

Wang

Liyanage

2022

IEEE Internet Things J.

213

View full text Add to dashboard Cite

The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ Unmanned Aerial Vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. This paper provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue operations, acting as aerial wireless BSs and drone light shows). As guidance for researchers and practitioners, the paper also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions.

show abstract

Section: E Computer Visionmentioning

confidence: 95%

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

McEnroe

Wang

Liyanage

2022

IEEE Internet Things J.

213

View full text Add to dashboard Cite

show abstract

“…Finally, a one-class support vector machine has been applied for classification. Recently, Castellano et al [40] proposed the use of a particular scheme of deep learning using a fully convolutional neural network (FCN) as a regressor for crowd counting applied to aerial scenes shot from UAVs. They train two FCNs simultaneously on the captured images of the crowd as well as the corresponding crowd heatmaps.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

Mehmood

2021

Applied Sciences

View full text Add to dashboard Cite

The increasing demand for surveillance systems has resulted in an unprecedented rise in the volume of video data being generated daily. The volume and frequency of the generation of video streams make it both impractical as well as inefficient to manually monitor them to keep track of abnormal events as they occur infrequently. To alleviate these difficulties through intelligent surveillance systems, several vision-based methods have appeared in the literature to detect abnormal events or behaviors. In this area, convolutional neural networks (CNNs) have also been frequently applied due to their prevalence in the related domain of general action recognition and classification. Although the existing approaches have achieved high detection rates for specific abnormal behaviors, more inclusive methods are expected. This paper presents a CNN-based approach that efficiently detects and classifies if a video involves the abnormal human behaviors of falling, loitering, and violence within uncrowded scenes. The approach implements a two-stream architecture using two separate 3D CNNs to accept a video and an optical flow stream as input to enhance the prediction performance. After applying transfer learning, the model was trained on a specialized dataset corresponding to each abnormal behavior. The experiments have shown that the proposed approach can detect falling, loitering, and violence with an accuracy of up to 99%, 97%, and 98%, respectively. The model achieved state-of-the-art results and outperformed the existing approaches.

show abstract

“…Although promising results have been presented, the techniques have only been verified in a reduced number of sample images. In [24], the authors have presented a Multiview CNN algorithm, which takes an RGB input coupled with artificially generated crowd heat maps. The authors have proposed that the algorithm can be implemented on a processing board available on commercial drones such as the NVIDIA Jetson TX2 board, ensuring its feasibility for real-time aerial crowd detection tasks.…”

Section: Crowd Detection and Monitoringmentioning

confidence: 99%

“…The crowd monitoring and analyses discussed in this paper can be further divided into several domains, namely crowd detection [8,[17][18][19][20][21][22], crowd counting [8,[23][24][25][26][27], crowd density estimation [17,26], crowd tracking [6,22,28,29], and crowd behavior analysis [30,31]. Whereas most works have focused on a single domain, recent results from the literature have discussed algorithms developed to tackle multiple domains such as the one presented in [32], and similar other areas will be further discussed in the proceeding literature.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial Vehicles for Crowd Monitoring and Analysis

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Multi-view Convolutional Network for Crowd Counting in Drone-Captured Images

Cited by 13 publications

References 23 publications

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges

Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks

Unmanned Aerial Vehicles for Crowd Monitoring and Analysis

Contact Info

Product

Resources

About