Alert button
Picture for Nan Jan

Nan Jan

Alert button

The 1st Tiny Object Detection Challenge:Methods and Results

Add code
Bookmark button
Alert button
Sep 16, 2020
Xuehui Yu, Zhenjun Han, Yuqi Gong, Nan Jan, Jian Zhao, Qixiang Ye, Jie Chen, Yuan Feng, Bin Zhang, Xiaodi Wang, Ying Xin, Jingwei Liu, Mingyuan Mao, Sheng Xu, Baochang Zhang, Shumin Han, Cheng Gao, Wei Tang, Lizuo Jin, Mingbo Hong, Yuchao Yang, Shuiwang Li, Huan Luo, Qijun Zhao, Humphrey Shi

Figure 1 for The 1st Tiny Object Detection Challenge:Methods and Results
Figure 2 for The 1st Tiny Object Detection Challenge:Methods and Results
Figure 3 for The 1st Tiny Object Detection Challenge:Methods and Results
Figure 4 for The 1st Tiny Object Detection Challenge:Methods and Results

The 1st Tiny Object Detection (TOD) Challenge aims toencourage research in developing novel and accurate methods for tinyobject detection in images which have wide views, with a current focuson tiny person detection. The TinyPerson dataset was used for the TODChallenge 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 meth-ods.The submission leaderboard will be reopened for researchers that areinterested in the TOD challenge. The benchmark dataset and other infor-mation can be found at: https://github.com/ucas-vg/TinyBenchmark.

* ECCV2020 Workshop on Real-world Computer Vision from Inputs with Limited Quality (RLQ) and Tiny Object Detection Challenge 
Viaarxiv icon
Github IconAccess Paper or Ask Questions
Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon