Conference Paper


Incremental tube construction for human action detection

Abstract

Current state-of-the-art action detection systems are tailored for offline batch-processing applications. However, for online applications like human-robot interaction, current systems fall short. In this work, we introduce a real-time and online joint-labelling and association algorithm for action detection that can incrementally construct space-time action tubes on the most challenging untrimmed action videos in which different action categories occur concurrently. In contrast to previous methods, we solve the linking, action labelling and temporal localization problems jointly in a single pass. We demonstrate superior online association accuracy and speed (1.8ms per frame) as compared to the current state-of-the-art offline and online systems.

Attached files

Authors

Singh Behl, Harkirat
Sapienza, Michael
Singh, Gurkirt
Saha, Suman
Cuzzolin, Fabio
Torr, Philip H.S.

Oxford Brookes departments

Faculty of Technology, Design and Environment\School of Engineering, Computing and Mathematics

Dates

Year of publication: 2018
Date of RADAR deposit: 2018-09-20



© 2018. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.


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This RADAR resource is the Version of Record of Incremental tube construction for human action detection

Details

  • Owner: Joseph Ripp
  • Collection: Outputs
  • Version: 1 (show all)
  • Status: Live