Tiny Object Tracking: A Large-scale Dataset and A Baseline
This addresses the challenge of tracking tiny objects with weak features in vision tasks, providing a dataset and baseline for future research, though it is incremental in nature.
The authors tackled the problem of tracking tiny objects in videos by creating a large-scale dataset with 434 sequences and over 217K frames, and proposed a Multilevel Knowledge Distillation Network (MKDNet) that outperformed state-of-the-art methods.
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection and segmentation. To promote the research and development of tiny object tracking, we create a large-scale video dataset, which contains 434 sequences with a total of more than 217K frames. Each frame is carefully annotated with a high-quality bounding box. In data creation, we take 12 challenge attributes into account to cover a broad range of viewpoints and scene complexities, and annotate these attributes for facilitating the attribute-based performance analysis. To provide a strong baseline in tiny object tracking, we propose a novel Multilevel Knowledge Distillation Network (MKDNet), which pursues three-level knowledge distillations in a unified framework to effectively enhance the feature representation, discrimination and localization abilities in tracking tiny objects. Extensive experiments are performed on the proposed dataset, and the results prove the superiority and effectiveness of MKDNet compared with state-of-the-art methods. The dataset, the algorithm code, and the evaluation code are available at https://github.com/mmic-lcl/Datasets-and-benchmark-code.