z-logo
open-access-imgOpen Access
Recognition of Transitional Action for Short-Term Action Prediction using Discriminative Temporal CNN Feature
Author(s) -
Hirokatsu Kataoka,
Yudai Miyashita,
Masaki Hayashi,
Kenji Iwata,
Yutaka Satoh
Publication year - 2016
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5244/c.30.12
Subject(s) - discriminative model , term (time) , computer science , feature (linguistics) , artificial intelligence , action (physics) , pattern recognition (psychology) , action recognition , feature extraction , speech recognition , linguistics , philosophy , physics , quantum mechanics , class (philosophy)
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom