Recognize multi-people interaction activity by PCA-HMMs

Ying Wang*, Xinwen Hou, Tieniu Tan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

3 Citations (Scopus)


In this paper, we propose a new approach to multi-people activity recognition in outdoor scenes. The proposed method is based on Hidden Markov Models with parameters of reduced dimensionality. Most existing work is based on HMMs and DBNs, and focuses on the interactions between two objects. However, longer feature vectors of HMMs usually lead to covariance matrix singularity in parameter learning and activity recognition. Moreover, arbitrary structure of DBNs can introduce large computational complexity. Compared with former works, the proposed method named PCA-HMMs reduces the dimensionality of the model parameters while retains most of the original variability, and thus avoids overflowing and weakens the constraints on observations in conventional HMMs. The experimental results proved that the modified HMMs are effective solutions for multi-people interactive activity recognition.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2006 - 7th Asian Conference on Computer Vision, Proceedings
Place of PublicationHyderabad, India
PublisherSpringer, Springer Nature
Number of pages10
Volume3851 LNCS
ISBN (Electronic)9783540324331
ISBN (Print)3540312196, 9783540312192
Publication statusPublished - 2006
Externally publishedYes
Event7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India
Duration: 13 Jan 200616 Jan 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3851 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Other7th Asian Conference on Computer Vision, ACCV 2006


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