Action classification by exploring directional co-occurrence of weighted STIPs
Human action recognition is challenging mainly due to intro-variety, inter-ambiguity and clutter backgrounds in real videos. Bag-of-visual words model utilizes spatio-temporal interest points(STIPs), and represents action by the distribution of points which ignores visual context among points. To ad...
Saved in:
Main Authors: | LIU, Mengyuan, LIU, Hong, SUN, Qianru |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2014
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/4463 https://ink.library.smu.edu.sg/context/sis_research/article/5466/viewcontent/ICIP2014_liumengyuan.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Learning spatio-temporal co-occurrence correlograms for efficient human action classification
by: SUN, Qianru, et al.
Published: (2013) -
Learning directional co-occurrence for human action classification
by: LIU, Hong, et al.
Published: (2014) -
Salient pairwise spatio-temporal interest points for real-time activity recognition
by: LIU, Mengyuan, et al.
Published: (2016) -
A compact representation of human actions by sliding coordinate coding
by: Ding, Runwei, et al.
Published: (2018) -
Human action classification based on sequential bag-of-words model
by: LIU, Hong, et al.
Published: (2014)