VastTrack: Vast Category Visual Object Tracking
Liang Peng Junyuan Gao Xinran Liu Weihong Li Shaohua Dong Zhipeng Zhang Heng Fan Libo Zhang
In this paper, we introduce a novel benchmark, dubbed VastTrack, towards
facilitating the development of more general visual tracking via encompassing
abundant classes and videos. VastTrack possesses several attractive properties:
(1) Vast Object Category. In particular, it covers target objects from 2,115
classes, largely surpassing object categories of existing popular benchmarks
(e.g., GOT-10k with 563 classes and LaSOT with 70 categories). With such vast
object classes, we expect to learn more general object tracking. (2) Larger
scale. Compared with current benchmarks, VastTrack offers 50,610 sequences with
4.2 million frames, which makes it to date the largest benchmark regarding the
number of videos, and thus could benefit training even more powerful visual
trackers in the deep learning era. (3) Rich Annotation. Besides conventional
bounding box annotations, VastTrack also provides linguistic descriptions for
the videos. The rich annotations of VastTrack enables development of both the
vision-only and the vision-language tracking. To ensure precise annotation, all
videos are manually labeled with multiple rounds of careful inspection and
refinement. To understand performance of existing trackers and to provide
baselines for future comparison, we extensively assess 25 representative
trackers. The results, not surprisingly, show significant drops compared to
those on current datasets due to lack of abundant categories and videos from
diverse scenarios for training, and more efforts are required to improve
general tracking. Our VastTrack and all the evaluation results will be made
publicly available https://github.com/HengLan/VastTrack.