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Found 9 papers, 4 papers with code
Towards Discriminative Representations with Contrastive Instances for Real-Time UAV Tracking
Lightweight Deep learning (DL)-based trackers can achieve a good balance between efficiency and precision but performance gains are limited by the compression rate.
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking
Efficiency has been a critical problem in UAV tracking due to limitations in computation resources, battery capacity, and unmanned aerial vehicle maximum load.
Adaptive and Background-Aware Vision Transformer for Real-Time UAV Tracking
In this paper, we propose an efficient ViT-based tracking framework, Aba-ViTrack, for UAV tracking.
Tracking Small and Fast Moving Objects: A Benchmark
The evaluation results exhibit that more effort are required to improve tracking small and fast moving objects.
Rank-Based Filter Pruning for Real-Time UAV Tracking
Model compression is a promising way to narrow the gap (i. e., effciency, precision) between DCF- and deep learning- based trackers, which has not caught much attention in UAV tracking.
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images
With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects.
Equivalence of Correlation Filter and Convolution Filter in Visual Tracking
On the surface, correlation filter and convolution filter are usually used for different purposes.
Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking
Unmanned aerial vehicle (UAV)-based tracking is attracting increasing attention and developing rapidly in applications such as agriculture, aviation, navigation, transportation and public security.
The 1st Tiny Object Detection Challenge:Methods and Results
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection.
