Human action recognition by extracting motion trajectories

Yuwen Fu; Shangpeng Yang

doi:10.1117/12.2196929

6 July 2015 Human action recognition by extracting motion trajectories

Yuwen Fu, Shangpeng Yang

Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96311H (2015) https://doi.org/10.1117/12.2196929
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States

Abstract

This paper proposes a novel human action recognition framework named Hidden Markov Model (HMM) based Hybrid Event Probability Sequence (HEPS), which can recognize unlabeled actions from videos. First, motion trajectories are effectively extracted using the centers of moving objects. Secondly, the HEPS is constructed using the trajectories and represents different human actions. Finally, the improved Particle Swarm Optimization (PSO) with inertia weight is introduced to recognize human actions using HMM. The proposed methods are evaluated on UCF Human Action Dataset and achieve 76.67% accurate rate. The comparative experiments results demonstrate that the HMM got superior results with HEPS and PSO.

Citation Download Citation

Yuwen Fu and Shangpeng Yang "Human action recognition by extracting motion trajectories", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96311H (6 July 2015); https://doi.org/10.1117/12.2196929

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available