Recognition of Transitional Action for Short-Term Action Prediction using Discriminative Temporal CNN Feature

نویسندگان

  • Hirokatsu Kataoka
  • Yudai Miyashita
  • Masaki Hayashi
  • Kenji Iwata
  • Yutaka Satoh
چکیده

Herein, we address transitional actions class as a class between actions. Transitional actions should be useful for producing short-term action predictions while an action is transitive. However, transitional action recognition is difficult because actions and transitional actions partially overlap each other. To deal with this issue, we propose a subtle motion descriptor (SMD) that identifies the sensitive differences between actions and transitional actions. The two primary contributions in this paper are as follows: (i) defining transitional actions for short-term action predictions that permit earlier predictions than early action recognition, and (ii) utilizing convolutional neural network (CNN) based SMD to present a clear distinction between actions and transitional actions.Using three different datasets, we will show that our proposed approach produces better results than do other state-of-the-art models. The experimental results clearly show the recognition performance effectiveness of our proposed model, as well as its ability to comprehend temporal motion in transitional actions.

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تاریخ انتشار 2016