Temporal modelling of first-person actions using hand-centric verb and object streams

Zeynep Gökce, Selen Pehlivan*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Analysis of first-person (egocentric) videos involving human actions could help in the solutions of many problems. These videos include a large number of fine-grained action categories with hand–object interactions. In this paper, a compositional verb–noun model including two complementary temporal streams is proposed with various fusion strategies to recognize egocentric actions. The first step is based on construction of verb and object video models as decomposition of actions with a special attention on hands. Particularly, the verb video model that is the spatial–temporal encoding of hand actions and the object video model that is the object scores with hand–object layout are represented as two separate pathways. The second step is the fusion stage to identify action category, where distinct verb and object models are combined to give their action judgments. We propose fusion strategies with recurrent steps collecting verb and object label judgments along a temporal video sequence. We evaluate recognition performances for individual verb and object models; and we present extensive experimental evaluations for action recognition over recurrent-based fusion approaches on the EGTEA Gaze+ dataset.

Original languageEnglish
Article number116436
Number of pages17
Publication statusPublished - Nov 2021
MoE publication typeA1 Journal article-refereed


  • Action recognition
  • Egocentric vision
  • First-person vision
  • RNN
  • Temporal models


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