Recognition of human group activity for video analytics

Jaeyong Ju; Cheoljong Yang; Sebastian Scherer; Hanseok Ko

doi:10.1007/978-3-319-24078-7_16

Recognition of human group activity for video analytics

Jaeyong Ju, Cheoljong Yang, Sebastian Scherer, Hanseok Ko

School of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people having complex interactions is still a formidable challenge. In this paper, a novel human group activity recognition method is proposed to deal with complex situation where there are multiple sub-groups. To characterize the inherent interactions of intra-subgroups and inter-subgroups with the varying number of participants, this paper proposes three types of group-activity descriptor using motion trajectory and appearance information of people. Experimental results on a public human group activity dataset demonstrate effectiveness of the proposed method.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag
Pages	161-169
Number of pages	9
Volume	9315
ISBN (Print)	9783319240770
DOIs	https://doi.org/10.1007/978-3-319-24078-7_16
Publication status	Published - 2015
Event	16th Pacific-Rim Conference on Multimedia, PCM 2015 - Gwangju, Korea, Republic of Duration: 2015 Sep 16 → 2015 Sep 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9315
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Other

Other	16th Pacific-Rim Conference on Multimedia, PCM 2015
Country	Korea, Republic of
City	Gwangju
Period	15/9/16 → 15/9/18

Keywords

Activity recognition
Human group activity
Video analytics

ASJC Scopus subject areas

Computer Science(all)
Theoretical Computer Science

Access to Document

10.1007/978-3-319-24078-7_16

Fingerprint Dive into the research topics of 'Recognition of human group activity for video analytics'. Together they form a unique fingerprint.

Cite this

Ju, J., Yang, C., Scherer, S., & Ko, H. (2015). Recognition of human group activity for video analytics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9315, pp. 161-169). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9315). Springer Verlag. https://doi.org/10.1007/978-3-319-24078-7_16

Recognition of human group activity for video analytics. / Ju, Jaeyong; Yang, Cheoljong; Scherer, Sebastian; Ko, Hanseok.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9315 Springer Verlag, 2015. p. 161-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9315).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ju, J, Yang, C, Scherer, S & Ko, H 2015, Recognition of human group activity for video analytics. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9315, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9315, Springer Verlag, pp. 161-169, 16th Pacific-Rim Conference on Multimedia, PCM 2015, Gwangju, Korea, Republic of, 15/9/16. https://doi.org/10.1007/978-3-319-24078-7_16

Ju J, Yang C, Scherer S, Ko H. Recognition of human group activity for video analytics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9315. Springer Verlag. 2015. p. 161-169. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-24078-7_16

Ju, Jaeyong ; Yang, Cheoljong ; Scherer, Sebastian ; Ko, Hanseok. / Recognition of human group activity for video analytics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9315 Springer Verlag, 2015. pp. 161-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{55f79759b8744e278d0f6fc60cdba5af,

title = "Recognition of human group activity for video analytics",

abstract = "Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people having complex interactions is still a formidable challenge. In this paper, a novel human group activity recognition method is proposed to deal with complex situation where there are multiple sub-groups. To characterize the inherent interactions of intra-subgroups and inter-subgroups with the varying number of participants, this paper proposes three types of group-activity descriptor using motion trajectory and appearance information of people. Experimental results on a public human group activity dataset demonstrate effectiveness of the proposed method.",

keywords = "Activity recognition, Human group activity, Video analytics",

author = "Jaeyong Ju and Cheoljong Yang and Sebastian Scherer and Hanseok Ko",

year = "2015",

doi = "10.1007/978-3-319-24078-7_16",

language = "English",

isbn = "9783319240770",

volume = "9315",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "161--169",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

note = "16th Pacific-Rim Conference on Multimedia, PCM 2015 ; Conference date: 16-09-2015 Through 18-09-2015",

}

TY - GEN

T1 - Recognition of human group activity for video analytics

AU - Ju, Jaeyong

AU - Yang, Cheoljong

AU - Scherer, Sebastian

AU - Ko, Hanseok

PY - 2015

Y1 - 2015

N2 - Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people having complex interactions is still a formidable challenge. In this paper, a novel human group activity recognition method is proposed to deal with complex situation where there are multiple sub-groups. To characterize the inherent interactions of intra-subgroups and inter-subgroups with the varying number of participants, this paper proposes three types of group-activity descriptor using motion trajectory and appearance information of people. Experimental results on a public human group activity dataset demonstrate effectiveness of the proposed method.

AB - Human activity recognition is an important and challenging task for video content analysis and understanding. Individual activity recognition has been well studied recently. However, recognizing the activities of human group with more than three people having complex interactions is still a formidable challenge. In this paper, a novel human group activity recognition method is proposed to deal with complex situation where there are multiple sub-groups. To characterize the inherent interactions of intra-subgroups and inter-subgroups with the varying number of participants, this paper proposes three types of group-activity descriptor using motion trajectory and appearance information of people. Experimental results on a public human group activity dataset demonstrate effectiveness of the proposed method.

KW - Activity recognition

KW - Human group activity

KW - Video analytics

UR - http://www.scopus.com/inward/record.url?scp=84951873430&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84951873430&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-24078-7_16

DO - 10.1007/978-3-319-24078-7_16

M3 - Conference contribution

AN - SCOPUS:84951873430

SN - 9783319240770

VL - 9315

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 161

EP - 169

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

T2 - 16th Pacific-Rim Conference on Multimedia, PCM 2015

Y2 - 16 September 2015 through 18 September 2015

ER -