The 1st Tiny Object Detection Challenge:Methods and Results
This work addresses the challenge of tiny object detection for computer vision researchers, but it is incremental as it builds on existing detection frameworks by introducing a new benchmark and competition.
The paper introduces the 1st Tiny Object Detection Challenge, which tackled the problem of detecting tiny objects in wide-view images, specifically focusing on tiny person detection using the TinyPerson dataset with 1,610 images and 72,651 annotations, and involved 36 teams competing to develop novel methods.
The 1st Tiny Object Detection (TOD) Challenge aims to encourage research in developing novel and accurate methods for tiny object detection in images which have wide views, with a current focus on tiny person detection. The TinyPerson dataset was used for the TOD Challenge and is publicly released. It has 1610 images and 72651 box-levelannotations. Around 36 participating teams from the globe competed inthe 1st TOD Challenge. In this paper, we provide a brief summary of the1st TOD Challenge including brief introductions to the top three methods.The submission leaderboard will be reopened for researchers that areinterested in the TOD challenge. The benchmark dataset and other information can be found at: https://github.com/ucas-vg/TinyBenchmark.