NetTrack: Tracking Highly Dynamic Objects with Fine-Grained Learning
Core Concepts
NetTrack introduces fine-grained learning to track highly dynamic objects in open-world scenarios.
Abstract
NetTrack proposes a tracking framework that leverages fine-grained visual cues and object-text correspondence for dynamicity-aware association and localization. The work also introduces the BFT dataset, a benchmark for evaluating highly dynamic object tracking. Extensive evaluations on various benchmarks demonstrate the effectiveness and generalization ability of NetTrack without the need for finetuning.
Structure:
Introduction to NetTrack and challenges in MOT.
Proposed methodology: Fine-Grained Net for dynamicity-aware association and object-text correspondence for localization.
Introduction of BFT dataset for evaluation.
Experimental results showcasing the performance of NetTrack on different benchmarks.
Ablation studies demonstrating the generality of fine-grained Nets and detachable modules.
Conclusion highlighting the contributions and future directions.
NetTrack
Stats
"BFT is particularly notable for the complex and unpredictable dynamicity of 22 bird species."
"The proposed NetTrack framework reaches SoTA performance in tracking highly dynamic objects in BFT."
"NetTrack surpasses tracking baselines on several challenging open-world MOT benchmarks."
"NetTrack achieves superior performance compared to SoTA finetuned closed-set trackers."
Quotes
"Most methods that solely depend on coarse-grained object cues are susceptible to degradation due to distorted internal relationships of dynamic objects."
"NetTrack constructs a dynamicity-aware association with a fine-grained Net, leveraging point-level visual cues."