Search Results for author:
Found 18 papers, 6 papers with code
Convolutional Channel-wise Competitive Learning for the Forward-Forward Algorithm
The Forward-Forward (FF) Algorithm has been recently proposed to alleviate the issues of backpropagation (BP) commonly used to train deep neural networks.
Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent
This work considers the problem of passively monitoring multiple moving targets with a single unmanned aerial vehicle (UAV) agent equipped with a direction-finding radar.
Integrated Guidance and Gimbal Control for Coverage Planning With Visibility Constraints
This work proposes an integrated guidance and gimbal control coverage path planning (CPP) approach, in which the mobility and gimbal inputs of an autonomous UAV agent are jointly controlled and optimized to achieve full coverage of a given object of interest, according to a specified set of optimality criteria.
Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D).
A Cooperative Multi-Agent Probabilistic Framework for Search and Track Missions
In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed.
Jointly-Optimized Searching and Tracking with Random Finite Sets
In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents.
Probabilistic Search and Track with Multiple Mobile Agents
In this paper we are interested in the task of searching and tracking multiple moving targets in a bounded surveillance area with a group of autonomous mobile agents.
Decentralized Search and Track with Multiple Autonomous Agents
Based on this, we develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring
Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies.
Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing
While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several missions, their inability of reliable and consistent autonomous landing poses a major setback for deploying such systems truly autonomously.
DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder
We explore the space of different autoencoder architectures and evaluate them on a diverse dataset created with real and synthetic images demonstrating that by exploiting spatio-temporal information combined with multi-component loss we significantly increase robustness against adverse image effects reaching within 5-6% of that of the original model on clean images.
EmergencyNet: Efficient Aerial Image Classification for Drone-Based Emergency Monitoring Using Atrous Convolutional Feature Fusion
Deep learning-based algorithms can provide state-of-the-art accuracy for remote sensing technologies such as unmanned aerial vehicles (UAVs)/drones, potentially enhancing their remote sensing capabilities for many emergency response and disaster management applications.
Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead
Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities.
Extracting the fundamental diagram from aerial footage
At large, this system behavior is characterized through the fundamental diagram of a road segment, a region or the network.
Efficient ConvNet-based Object Detection for Unmanned Aerial Vehicles by Selective Tile Processing
Many applications utilizing Unmanned Aerial Vehicles (UAVs) require the use of computer vision algorithms to analyze the information captured from their on-board camera.
EdgeNet: Balancing Accuracy and Performance for Edge-based Convolutional Neural Network Object Detectors
Moreover, a use-case for pedestrian detection from Unmanned-Areal-Vehicle (UAV) is presented showing the impact that the proposed approach has on sensitivity, average processing time and power consumption when is implemented on different platforms.
Deep-Learning-Based Aerial Image Classification for Emergency Response Applications Using Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs), equipped with camera sensors can facilitate enhanced situational awareness for many emergency response and disaster management applications since they are capable of operating in remote and difficult to access areas.
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%.
Object Detection In Aerial Images
One-Shot Object Detection
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