739 research outputs found
Using Deep Networks for Drone Detection
Drone detection is the problem of finding the smallest rectangle that
encloses the drone(s) in a video sequence. In this study, we propose a solution
using an end-to-end object detection model based on convolutional neural
networks. To solve the scarce data problem for training the network, we propose
an algorithm for creating an extensive artificial dataset by combining
background-subtracted real images. With this approach, we can achieve precision
and recall values both of which are high at the same time.Comment: To appear in International Workshop on Small-Drone Surveillance,
Detection and Counteraction Techniques organised within AVSS 201
Survey on video anomaly detection in dynamic scenes with moving cameras
The increasing popularity of compact and inexpensive cameras, e.g.~dash
cameras, body cameras, and cameras equipped on robots, has sparked a growing
interest in detecting anomalies within dynamic scenes recorded by moving
cameras. However, existing reviews primarily concentrate on Video Anomaly
Detection (VAD) methods assuming static cameras. The VAD literature with moving
cameras remains fragmented, lacking comprehensive reviews to date. To address
this gap, we endeavor to present the first comprehensive survey on Moving
Camera Video Anomaly Detection (MC-VAD). We delve into the research papers
related to MC-VAD, critically assessing their limitations and highlighting
associated challenges. Our exploration encompasses three application domains:
security, urban transportation, and marine environments, which in turn cover
six specific tasks. We compile an extensive list of 25 publicly-available
datasets spanning four distinct environments: underwater, water surface,
ground, and aerial. We summarize the types of anomalies these datasets
correspond to or contain, and present five main categories of approaches for
detecting such anomalies. Lastly, we identify future research directions and
discuss novel contributions that could advance the field of MC-VAD. With this
survey, we aim to offer a valuable reference for researchers and practitioners
striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie
Deep Learning Computer Vision Algorithms for Real-time UAVs On-board Camera Image Processing
This paper describes how advanced deep learning based computer vision
algorithms are applied to enable real-time on-board sensor processing for small
UAVs. Four use cases are considered: target detection, classification and
localization, road segmentation for autonomous navigation in GNSS-denied zones,
human body segmentation, and human action recognition. All algorithms have been
developed using state-of-the-art image processing methods based on deep neural
networks. Acquisition campaigns have been carried out to collect custom
datasets reflecting typical operational scenarios, where the peculiar point of
view of a multi-rotor UAV is replicated. Algorithms architectures and trained
models performances are reported, showing high levels of both accuracy and
inference speed. Output examples and on-field videos are presented,
demonstrating models operation when deployed on a GPU-powered commercial
embedded device (NVIDIA Jetson Xavier) mounted on board of a custom quad-rotor,
paving the way to enabling high level autonomy.Comment: 10 pages, 12 figures, NATO AVT-353 Research Workshop "Artificial
Intelligence in Cockpits for UAVs", Turin, Italy, 26 April 202
Spatio-temporal road detection from aerial imagery using CNNs
The main goal of this paper is to detect roads from aerial imagery recorded by drones. To achieve this, we
propose a modification of SegNet, a deep fully convolutional neural network for image segmentation. In
order to train this neural network, we have put together a database containing videos of roads from the point
of view of a small commercial drone. Additionally, we have developed an image annotation tool based on
the watershed technique, in order to perform a semi-automatic labeling of the videos in this database. The
experimental results using our modified version of SegNet show a big improvement on the performance of the
neural network when using aerial imagery, obtaining over 90% accuracy.Postprint (published version
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