Search Results for author:
Found 35 papers, 32 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.
Towards Real-World Visual Tracking with Temporal Contexts
To handle those problems, we propose a two-level framework (TCTrack) that can exploit temporal contexts efficiently.
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.
Continuity-Aware Latent Interframe Information Mining for Reliable UAV Tracking
To address the above issues, this work proposes a novel framework with continuity-aware latent interframe information mining for reliable UAV tracking, i. e., ClimRT.
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.
PVT++: A Simple End-to-End Latency-Aware Visual Tracking Framework
Visual object tracking is essential to intelligent robots.
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.
Local Perception-Aware Transformer for Aerial Tracking
Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder.
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.
Ad2Attack: Adaptive Adversarial Attack on Real-Time UAV Tracking
Therefore, to help increase awareness of the potential risk and the robustness of UAV tracking, this work proposes a novel adaptive adversarial attack approach, i. e., Ad$^2$Attack, against UAV object tracking.
TCTrack: Temporal Contexts for Aerial Tracking
Temporal contexts among consecutive frames are far from being fully utilized in existing visual trackers.
Real-Time Monocular Human Depth Estimation and Segmentation on Embedded Systems
Estimating a scene's depth to achieve collision avoidance against moving pedestrians is a crucial and fundamental problem in the robotic field.
HiFT: Hierarchical Feature Transformer for Aerial Tracking
Most existing Siamese-based tracking methods execute the classification and regression of the target object based on the similarity maps.
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.
SiamAPN++: Siamese Attentional Aggregation Network for Real-Time UAV Tracking
By virtue of the attention mechanism, we conduct a special attentional aggregation network (AAN) consisting of self-AAN and cross-AAN for raising the representation ability of features eventually.
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.
ADTrack: Target-Aware Dual Filter Learning for Real-Time Anti-Dark UAV Tracking
The target-aware mask can be applied to jointly train a target-focused filter that assists the context filter for robust tracking.
Predictive Visual Tracking: A New Benchmark and Baseline Approach
As a crucial robotic perception capability, visual tracking has been intensively studied recently.
All-Day Object Tracking for Unmanned Aerial Vehicle
However, prior tracking methods have merely focused on robust tracking in the well-illuminated scenes, while ignoring trackers' capabilities to be deployed in the dark.
Siamese Anchor Proposal Network for High-Speed Aerial Tracking
In the domain of visual tracking, most deep learning-based trackers highlight the accuracy but casting aside efficiency.
Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation
Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field.
Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization
Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement.
DR^2Track: Towards Real-Time Visual Tracking for UAV via Distractor Repressed Dynamic Regression
By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity.
Learning Consistency Pursued Correlation Filters for Real-Time UAV Tracking
By minimizing the difference between the practical and the scheduled ideal consistency map, the consistency level is constrained to maintain temporal smoothness, and rich temporal information contained in response maps is introduced.
Towards Robust Visual Tracking for Unmanned Aerial Vehicle with Tri-Attentional Correlation Filters
Object tracking has been broadly applied in unmanned aerial vehicle (UAV) tasks in recent years.
AutoTrack: Towards High-Performance Visual Tracking for UAV with Automatic Spatio-Temporal Regularization
Considerable tests in the indoor practical scenarios have proven the effectiveness and versatility of our localization method.
Keyfilter-Aware Real-Time UAV Object Tracking
Correlation filter-based tracking has been widely applied in unmanned aerial vehicle (UAV) with high efficiency.
Training-Set Distillation for Real-Time UAV Object Tracking
After the establishment of a new slot, the weighted fusion of the previous samples generates one key-sample, in order to reduce the number of samples to be scored.
Augmented Memory for Correlation Filters in Real-Time UAV Tracking
The outstanding computational efficiency of discriminative correlation filter (DCF) fades away with various complicated improvements.
Boundary Effect-Aware Visual Tracking for UAV with Online Enhanced Background Learning and Multi-Frame Consensus Verification
Meanwhile, convolutional features are extracted to provide a more comprehensive representation of the object.
Learning Aberrance Repressed Correlation Filters for Real-Time UAV Tracking
Traditional framework of discriminative correlation filters (DCF) is often subject to undesired boundary effects.
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services.
