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Found 10 papers, 9 papers with code
NetTrack: Tracking Highly Dynamic Objects with a Net
Most methods that solely depend on coarse-grained object cues, such as boxes and the overall appearance of the object, are susceptible to degradation due to distorted internal relationships of dynamic objects.
SAM-DA: UAV Tracks Anything at Night with SAM-Powered Domain Adaptation
However, the state-of-the-art (SOTA) DA still lacks the potential object with accurate pixel-level location and boundary to generate the high-quality target domain training sample.
Tracker Meets Night: A Transformer Enhancer for UAV Tracking
To realize reliable UAV tracking at night, a spatial-channel Transformer-based low-light enhancer (namely SCT), which is trained in a novel task-inspired manner, is proposed and plugged prior to tracking approaches.
SGDViT: Saliency-Guided Dynamic Vision Transformer for UAV Tracking
The proposed method designs a new task-specific object saliency mining network to refine the cross-correlation operation and effectively discriminate foreground and background information.
Siamese Object Tracking for Vision-Based UAM Approaching with Pairwise Scale-Channel Attention
The key to the visual UAM approaching lies in object tracking, while current UAM tracking typically relies on costly model-based methods.
HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking
Pixel-level range mask is introduced to make HighlightNet more focused on the enhancement of the tracking object and regions without light sources.
Siamese Object Tracking for Unmanned Aerial Vehicle: A Review and Comprehensive Analysis
Unmanned aerial vehicle (UAV)-based visual object tracking has enabled a wide range of applications and attracted increasing attention in the field of intelligent transportation systems because of its versatility and effectiveness.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking
Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications.
DarkLighter: Light Up the Darkness for UAV Tracking
Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems.
Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label
However, prevalent discriminative correlation filter (DCF) based trackers are insensitive to target mutations due to a predefined label, which concentrates on merely the centre of the training region.
