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, Anmoll Kumar Jain, Alfredo, Costilla Reyes, Na Zou, Xia Hu
TL;DR
AutoVideo is a Python-based system that automates video action recognition by providing modular pipeline construction, data-driven tuning, and a user-friendly GUI, simplifying the development process.
Contribution
It introduces a highly modular, extendable framework with comprehensive primitives and automated tuning, streamlining the creation of video action recognition solutions.
Findings
Automates pipeline construction and tuning for action recognition
Provides a user-friendly GUI for easy system interaction
Enables efficient development with extensive primitives
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
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Taxonomy
TopicsHuman Pose and Action Recognition · Anomaly Detection Techniques and Applications · Video Analysis and Summarization