Anti-UAV20
* *Catch UAVs That Want to Watch You: Detection and Tracking of Unmanned Aerial Vehicle in the Wild
* Effect of Annotation Errors on Drone Detection with YOLOv3
* IPG-Net: Image Pyramid Guidance Network for Small Object Detection
* Real-time Robust Approach for Tracking UAVs in Infrared Videos, A
* Real-time Tracking with Stabilized Frame
Anti-UAV21
* *Catch UAVs That Want to Watch You: Detection and Tracking of Unmanned Aerial Vehicle in the Wild
* Real-time Anti-distractor Infrared UAV Tracker with Channel Feature Refinement Module, A
* Semi-Automatic Annotation For Visual Object Tracking
* SiamSTA: Spatio-Temporal Attention based Siamese Tracker for Tracking UAVs
* Unified Approach for Tracking UAVs in Infrared, A
* Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural Networks: A Performance Benchmark
Anti-UAV23
* *Catch UAVs That Want to Watch You: Detection and Tracking of Unmanned Aerial Vehicle in the Wild
* Global-Local Tracking Framework Driven by Both Motion and Appearance for Infrared Anti-UAV, A
* Motion Matters: Difference-based Multi-scale Learning for Infrared UAV Detection
* Real-time and Lightweight Method for Tiny Airborne Object Detection, A
* Strong Detector with Simple Tracker
* Unified Transformer-based Tracker for Anti-UAV Tracking, A
* Video Tiny-Object Detection Guided by the Spatial-Temporal Motion Information
7 for Anti-UAV23
Anti-UAV25
* *Catch UAVs That Want to Watch You: Detection and Tracking of Unmanned Aerial Vehicle in the Wild
* Detection and Localization of Drones and UAVs Using Sound and Vision
* Dist-Tracker: A Small Object-Aware Detector and Tracker for UAV Tracking
* DLST: Dual-Template Co-Evolution Learning for Robust Long-Term Drone Tracking in Dynamic Environments
* Enhancing Few-Shot Class-Incremental Learning via Frozen Feature Augmentation
* Power of Augmentations in IR Object Detection, The
* PPTracker: Tracking UAV Swarms with Prior Prompt
* Securing the Skies: a Comprehensive Survey on Anti-Uav Methods, Benchmarking, and Future Directions
* Simple Detector with Frame Dynamics is a Strong Tracker, A
* Strong Baseline: Multi-UAV Tracking via YOLOv12 with BoT-SORT-ReID
* StrongSiamTracker: A Siamese Tracker with Dynamic Global Detection for Robust Anti-UAV Tracking
11 for Anti-UAV25
AnticipateBeh18
* *Anticipating Human Behavior
* Action Alignment from Gaze Cues in Human-Human and Human-Robot Interaction
* Action Anticipation by Predicting Future Dynamic Images
* Context Graph Based Video Frame Prediction Using Locally Guided Objective
* Convolutional Neural Network for Trajectory Prediction
* Forecasting Hands and Objects in Future Frames
* Group LSTM: Group Trajectory Prediction in Crowded Scenarios
* Joint Future Semantic and Instance Segmentation Prediction
* Predicting Action Tubes
* RED: A Simple but Effective Baseline Predictor for the TrajNet Benchmark
10 for AnticipateBeh18