物体事前分布を活用した点追跡
Leveraging Object Priors for Point Tracking
点追跡において、点が物体の外に出てしまう問題を解決するため、学習時に物体の境界内に留まるよう誘導する物体性正則化を提案し、3つのベンチマークで最高性能を達成した。
著者: Bikram Boote, Anh Thai, Wenqi Jia, Ozgur Kara, Stefan Stojanov, James M. Rehg, Sangmin Lee
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Point tracking is a fundamental problem in computer vision with numerous applications in AR and robotics. A common failure mode in long-term point tracking occurs when the predicted point leaves the object it belongs to and lands on the background or another object. We identify this as the failure to correctly capture objectness properties in learning to track. To address this limitation of prior work, we propose a novel objectness regularization approach that guides points to be aware of object priors by forcing them to stay inside the the boundaries of object instances. By capturing objectness cues at training time, we avoid the need to compute object masks during testing. In addition, we leverage contextual attention to enhance the feature representation for capturing objectness at the feature level more effectively. As a result, our approach achieves state-of-the-art performance on three point tracking benchmarks, and we further validate the effectiveness of our components via ablation studies. The source code is available at: https://github.com/RehgLab/tracking_objectness
