Search Results for author:
Found 9 papers, 3 papers with code
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.
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
Late or Earlier Information Fusion from Depth and Spectral Data? Large-Scale Digital Surface Model Refinement by Hybrid-cGAN
We present the workflow of a DSM refinement methodology using a Hybrid-cGAN where the generative part consists of two encoders and a common decoder which blends the spectral and height information within one network.
DSM Building Shape Refinement from Combined Remote Sensing Images based on Wnet-cGANs
We describe the workflow of a digital surface models (DSMs) refinement algorithm using a hybrid conditional generative adversarial network (cGAN) where the generative part consists of two parallel networks merged at the last stage forming a WNet architecture.
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)
Aerial LaneNet: Lane Marking Semantic Segmentation in Aerial Imagery using Wavelet-Enhanced Cost-sensitive Symmetric Fully Convolutional Neural Networks
Therefore, accurate and reliable lane marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals.
Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data
Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field.
Authorship Analysis based on Data Compression
This paper proposes to perform authorship analysis using the Fast Compression Distance (FCD), a similarity measure based on compression with dictionaries directly extracted from the written texts.
