The 2020 Low-Power Computer Vision Challenge

Hu, Xiao; Chang, Ming-Ching; Chen, Yuwei; Sridhar, Rahul; Hu, Zhenyu; Xue, Yunhe; Wu, Zhenyu; Pi, Pengcheng; Shen, Jiayi; Tan, Jianchao; Lian, Xiangru; Liu, Ji; Wang, Zhangyang; Liu, Chia-Hsiang; Han, Yu-Shin; Sung, Yuan-Yao; Lee, Yi; Wu, Kai–Chiang; Guo, Weixiang; Lee, Richard T.; Liang, Shengwen; Wang, Zerun; Ding, Guiguang; Zhang, Gang; Xi, Teng; Chen, Yubei; Cai, Han; Zhu, Lei; Zhang, Zhekai; Han, Song; Jeong, Seonghwan; Kwon, Young‐Min; Wang, Tianzhe; Pan, Jeffery

doi:10.1109/aicas51828.2021.9458522

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2022

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“…The 2021 Low-Power Computer Vision Challenge (LPCVC) UAV-Video Track [24,25] requires contestants to spatio-temporally localize the key action of ball-catching from videos captured by drones. The evaluation metric takes both accuracy and efficiency into account.…”

Section: Introductionmentioning

confidence: 99%

E^2TAD: An Energy-Efficient Tracking-based Action Detector

Hu¹,

Wu²,

Miao³

et al. 2022

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Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).

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Section: Introductionmentioning

confidence: 99%