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Found 6 papers, 1 papers with code
Drones4Good: Supporting Disaster Relief Through Remote Sensing and AI
In order to respond effectively in the aftermath of a disaster, emergency services and relief organizations rely on timely and accurate information about the affected areas.
Multiple Pedestrians and Vehicles Tracking in Aerial Imagery: A Comprehensive Study
We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for a more accurate and stable tracking.
EAGLE: Large-scale Vehicle Detection Dataset in Real-World Scenarios using Aerial Imagery
To address this issue, we introduce EAGLE (oriEnted vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale dataset for multi-class vehicle detection with object orientation information in aerial imagery.
AerialMPTNet: Multi-Pedestrian Tracking in Aerial Imagery Using Temporal and Graphical Features
Due to the challenges such as the large number and the tiny size of the pedestrians (e. g., 4 x 4 pixels) with their similar appearances as well as different scales and atmospheric conditions of the images with their extremely low frame rates (e. g., 2 fps), current state-of-the-art algorithms including the deep learning-based ones are unable to perform well.
MRCNet: Crowd Counting and Density Map Estimation in Aerial and Ground Imagery
As a remedy, in this work we introduce a novel crowd dataset, the DLR Aerial Crowd Dataset (DLR-ACD), which is composed of 33 large aerial images acquired from 16 flight campaigns over mass events with 226, 291 persons annotated.
Ranked #1 on
Crowd Counting
on DLR-ACD
Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery
During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular.
Ranked #49 on
Object Detection In Aerial Images
on DOTA
(using extra training data)
