Automatic Failure Recovery and Re-Initialization for Online UAV Tracking with Joint Scale and Aspect Ratio Optimization
원문 초록
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. In this work, a real-time UAV tracking algorithm with powerful size estimation ability is proposed. Specifically, the overall tracking task is allocated to two 2D filters: (i) translation filter for location prediction in the space domain, (ii) size filter for scale and aspect ratio optimization in the size domain. Besides, an efficient two-stage re-detection strategy is introduced for long-term UAV tracking tasks. Large-scale experiments on four UAV benchmarks demonstrate the superiority of the presented method which has computation feasibility on a low-cost CPU.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
CycleVAR: Repurposing Autoregressive Model for Unsupervised One-Step Image Translation
R1-Track: Direct Application of MLLMs to Visual Object Tracking via Reinforcement Learning
Exploiting Lightweight Hierarchical ViT and Dynamic Framework for Efficient Visual Tracking
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
LIGHTHOUSE: Fast and precise distance to shoreline calculations from anywhere on earth
토론
이 논문과 연결된 포럼 글이 아직 없습니다.