Bio-inspired probabilistic model for crowd emotion detection

M.W. Baig; E.I. Barakova; L. Marcenaro; C.S. Regazzoni; G.W.M. Rauterberg

doi:10.1109/IJCNN.2014.6889964

Bio-inspired probabilistic model for crowd emotion detection

M.W. Baig, E.I. Barakova, L. Marcenaro, C.S. Regazzoni, G.W.M. Rauterberg

Designed Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

7 Citations (Scopus)

Abstract

Detection of emotions of a crowd is a new research area, which has never, to our knowledge, been accounted for research in previous literature. A bio-inspired model for representation of emotional patterns in crowds has been demonstrated. Emotions have been defined as evolving patterns as part of a dynamic pattern of events. This model has been developed to detect emotions of a crowd based on the knowledge from a learned context, psychology and experience of people in crowd management. The emotions of multiple people making a crowd in any surveillance environment are estimated by sensors signals such as a camera and are being tracked and their behavior is modeled using bio-inspired dynamic model. The behavior changes correspond to changes in emotions. The proposed algorithm involves the probabilistic signal processing modelling techniques for analysis of different types of behavior, interaction detection and estimation of emotions. The emotions are recognized by the expectation of temporal occurrences of causal events modeled by Gaussian mixture model. The model has been evaluated using the simulated behavioral model of a crowd.

Original language	English
Title of host publication	Proceedings International Joint Conference on Neural Networks (IJCNN), 6-11 July, 2014, Beijing, China
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	3966-3973
ISBN (Print)	978-1-4799-1484-5
DOIs	https://doi.org/10.1109/IJCNN.2014.6889964
Publication status	Published - 2014
Event	2014 International Joint Conference on Neural Networks (IJCNN 2014), July 6-11, 2014, Beijing, China - Beijing International Convention Center, Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 http://www.ieee-wcci2014.org

Conference

Conference	2014 International Joint Conference on Neural Networks (IJCNN 2014), July 6-11, 2014, Beijing, China
Abbreviated title	IJCNN 2014
Country	China
City	Beijing
Period	6/07/14 → 11/07/14
Other	International Joint Conference on Neural Networks
Internet address	http://www.ieee-wcci2014.org

Access to Document

10.1109/IJCNN.2014.6889964

Fingerprint Dive into the research topics of 'Bio-inspired probabilistic model for crowd emotion detection'. Together they form a unique fingerprint.

Cite this

Baig, M. W., Barakova, E. I., Marcenaro, L., Regazzoni, C. S., & Rauterberg, G. W. M. (2014). Bio-inspired probabilistic model for crowd emotion detection. In Proceedings International Joint Conference on Neural Networks (IJCNN), 6-11 July, 2014, Beijing, China (pp. 3966-3973). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2014.6889964

@inproceedings{7494069d5e0a4c719ba371bdca65be5d,

title = "Bio-inspired probabilistic model for crowd emotion detection",

abstract = "Detection of emotions of a crowd is a new research area, which has never, to our knowledge, been accounted for research in previous literature. A bio-inspired model for representation of emotional patterns in crowds has been demonstrated. Emotions have been defined as evolving patterns as part of a dynamic pattern of events. This model has been developed to detect emotions of a crowd based on the knowledge from a learned context, psychology and experience of people in crowd management. The emotions of multiple people making a crowd in any surveillance environment are estimated by sensors signals such as a camera and are being tracked and their behavior is modeled using bio-inspired dynamic model. The behavior changes correspond to changes in emotions. The proposed algorithm involves the probabilistic signal processing modelling techniques for analysis of different types of behavior, interaction detection and estimation of emotions. The emotions are recognized by the expectation of temporal occurrences of causal events modeled by Gaussian mixture model. The model has been evaluated using the simulated behavioral model of a crowd.",

author = "M.W. Baig and E.I. Barakova and L. Marcenaro and C.S. Regazzoni and G.W.M. Rauterberg",

year = "2014",

doi = "10.1109/IJCNN.2014.6889964",

language = "English",

isbn = "978-1-4799-1484-5",

pages = "3966--3973",

booktitle = "Proceedings International Joint Conference on Neural Networks (IJCNN), 6-11 July, 2014, Beijing, China",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2014 International Joint Conference on Neural Networks (IJCNN 2014), July 6-11, 2014, Beijing, China, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",

url = "http://www.ieee-wcci2014.org",

}

Baig, MW, Barakova, EI, Marcenaro, L, Regazzoni, CS & Rauterberg, GWM 2014, Bio-inspired probabilistic model for crowd emotion detection. in Proceedings International Joint Conference on Neural Networks (IJCNN), 6-11 July, 2014, Beijing, China. Institute of Electrical and Electronics Engineers, Piscataway, pp. 3966-3973, 2014 International Joint Conference on Neural Networks (IJCNN 2014), July 6-11, 2014, Beijing, China, Beijing, China, 6/07/14. https://doi.org/10.1109/IJCNN.2014.6889964

Bio-inspired probabilistic model for crowd emotion detection. / Baig, M.W.; Barakova, E.I.; Marcenaro, L.; Regazzoni, C.S.; Rauterberg, G.W.M.

Proceedings International Joint Conference on Neural Networks (IJCNN), 6-11 July, 2014, Beijing, China. Piscataway : Institute of Electrical and Electronics Engineers, 2014. p. 3966-3973.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Bio-inspired probabilistic model for crowd emotion detection

AU - Baig, M.W.

AU - Barakova, E.I.

AU - Marcenaro, L.

AU - Regazzoni, C.S.

AU - Rauterberg, G.W.M.

PY - 2014

Y1 - 2014

N2 - Detection of emotions of a crowd is a new research area, which has never, to our knowledge, been accounted for research in previous literature. A bio-inspired model for representation of emotional patterns in crowds has been demonstrated. Emotions have been defined as evolving patterns as part of a dynamic pattern of events. This model has been developed to detect emotions of a crowd based on the knowledge from a learned context, psychology and experience of people in crowd management. The emotions of multiple people making a crowd in any surveillance environment are estimated by sensors signals such as a camera and are being tracked and their behavior is modeled using bio-inspired dynamic model. The behavior changes correspond to changes in emotions. The proposed algorithm involves the probabilistic signal processing modelling techniques for analysis of different types of behavior, interaction detection and estimation of emotions. The emotions are recognized by the expectation of temporal occurrences of causal events modeled by Gaussian mixture model. The model has been evaluated using the simulated behavioral model of a crowd.

AB - Detection of emotions of a crowd is a new research area, which has never, to our knowledge, been accounted for research in previous literature. A bio-inspired model for representation of emotional patterns in crowds has been demonstrated. Emotions have been defined as evolving patterns as part of a dynamic pattern of events. This model has been developed to detect emotions of a crowd based on the knowledge from a learned context, psychology and experience of people in crowd management. The emotions of multiple people making a crowd in any surveillance environment are estimated by sensors signals such as a camera and are being tracked and their behavior is modeled using bio-inspired dynamic model. The behavior changes correspond to changes in emotions. The proposed algorithm involves the probabilistic signal processing modelling techniques for analysis of different types of behavior, interaction detection and estimation of emotions. The emotions are recognized by the expectation of temporal occurrences of causal events modeled by Gaussian mixture model. The model has been evaluated using the simulated behavioral model of a crowd.

U2 - 10.1109/IJCNN.2014.6889964

DO - 10.1109/IJCNN.2014.6889964

M3 - Conference contribution

SN - 978-1-4799-1484-5

SP - 3966

EP - 3973

BT - Proceedings International Joint Conference on Neural Networks (IJCNN), 6-11 July, 2014, Beijing, China

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 2014 International Joint Conference on Neural Networks (IJCNN 2014), July 6-11, 2014, Beijing, China

Y2 - 6 July 2014 through 11 July 2014

ER -