Calculation of dense trajectory descriptors on a heterogeneous embedded architecture

2015 ◽  
Vol 61 (10) ◽  
pp. 659-667 ◽  
Author(s):  
J.R. Cózar ◽  
M.J. Marín-Jiménez ◽  
J.M. González-Linares ◽  
N. Guil ◽  
J. Gómez-Luna
Keyword(s):  
Dense Trajectory ◽  
Embedded Architecture

scholarly journals Embedded architecture description language

2010 ◽  
Vol 83 (2) ◽  
pp. 235-252 ◽  
Author(s):  
Juncao Li ◽  
Nicholas T. Pilkington ◽  
Fei Xie ◽  
Qiang Liu
Keyword(s):  
Description Language ◽  
Architecture Description Language ◽  
Embedded Architecture ◽  
Architecture Description

High-Performance Embedded Architecture and Compilation Roadmap

2007 ◽  
pp. 5-29 ◽  
Author(s):  
Koen De Bosschere ◽  
Wayne Luk ◽  
Xavier Martorell ◽  
Nacho Navarro ◽  
Mike O’Boyle ◽  
...  
Keyword(s):  
High Performance ◽  
Embedded Architecture

scholarly journals Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors

2019 ◽  
Vol 9 (10) ◽  
pp. 2126 ◽  
Author(s):  
Suge Dong ◽  
Daidi Hu ◽  
Ruijun Li ◽  
Mingtao Ge
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Recognition Method ◽  
Foreground Region ◽  
Dense Trajectory ◽  
Trajectory Method ◽  
Direction Information ◽  
Action Category

Aimed at the problems of high redundancy of trajectory and susceptibility to background interference in traditional dense trajectory behavior recognition methods, a human action recognition method based on foreground trajectory and motion difference descriptors is proposed. First, the motion magnitude of each frame is estimated by optical flow, and the foreground region is determined according to each motion magnitude of the pixels; the trajectories are only extracted from behavior-related foreground regions. Second, in order to better describe the relative temporal information between different actions, a motion difference descriptor is introduced to describe the foreground trajectory, and the direction histogram of the motion difference is constructed by calculating the direction information of the motion difference per unit time of the trajectory point. Finally, a Fisher vector (FV) is used to encode histogram features to obtain video-level action features, and a support vector machine (SVM) is utilized to classify the action category. Experimental results show that this method can better extract the action-related trajectory, and it can improve the recognition accuracy by 7% compared to the traditional dense trajectory method.

Sign in / Sign up

Export Citation Format

Share Document