AutoVideo: An Automated Video Action Recognition System
Daochen Zha, Zaid Pervaiz Bhat, Yi-Wei Chen, Yicheng Wang, Sirui Ding, Jiaben Chen, Kwei-Herng Lai, Mohammad Qazim Bhat
Abstract
Action recognition is an important task for video understanding with broad applications. However, developing an effective action recognition solution often requires extensive engineering efforts in building and testing different combinations of the modules and their hyperparameters. In this demo, we present AutoVideo, a Python system for automated video action recognition. AutoVideo is featured for 1) highly modular and extendable infrastructure following the standard pipeline language, 2) an exhaustive list of primitives for pipeline construction, 3) data-driven tuners to save the efforts of pipeline tuning, and 4) easy-to-use Graphical User Interface (GUI). AutoVideo is released under MIT license at https://github.com/datamllab/autovideo
BibTeX
@inproceedings{ijcai2022p862,
title = {AutoVideo: An Automated Video Action Recognition System},
author = {Zha, Daochen and Bhat, Zaid Pervaiz and Chen, Yi-Wei and Wang, Yicheng and Ding, Sirui and Chen, Jiaben and Lai, Kwei-Herng and Bhat, Mohammad Qazim and Jain, Anmoll Kumar and Costilla Reyes, Alfredo and Zou, Na and Hu, Xia},
booktitle = {Proceedings of the Thirty-First International Joint Conference on
Artificial Intelligence, {IJCAI-22}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Lud De Raedt},
pages = {5952--5955},
year = {2022},
month = {7},
note = {Demo Track},
doi = {10.24963/ijcai.2022/862},
url = {https://doi.org/10.24963/ijcai.2022/862},
}