Abstract
Perceiving crowd emotions and understand the situation is vital to control the situations in surveillance applications. This paper introduces the evolution of methods for crowd emotion perception based on bio-inspired probabilistic models. The emotions have been perceived both in an offline and online manner from the crowd. We focus on the perception of emotion from crowd behavior and dynamics. The paper explains few probabilistic algorithms and compares these for detection of emotion of crowds and proposes a probabilistic modelling approach which is trained on data to perceive the emotions of the crowd in an area under surveillance. Emotions are defined as evolving dynamic patterns arising due to interaction of people in an environment with their relationships to the past interaction patterns. Camera sensors are used to track the motion of the individuals within a crowd scenario under observation. The data mining techniques are used to distinguish between different behaviors and events into positive and negative emotions. The results have been evaluated using simulated data from a proposed office environment.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE International Conference on Digital Signal Processing, DSP 2015, 21-24 July 2015, Singapore |
| Place of Publication | Piscataway |
| Publisher | Institute of Electrical and Electronics Engineers |
| Pages | 703-707 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781479980581 |
| DOIs | |
| Publication status | Published - 9 Sep 2015 |
| Event | IEEE International Conference on Digital Signal Processing, DSP 2015 - Singapore, Singapore Duration: 21 Jul 2015 → 24 Jul 2015 |
Conference
| Conference | IEEE International Conference on Digital Signal Processing, DSP 2015 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 21/07/15 → 24/07/15 |
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Keywords
- Autobiographical Memory
- Crowd emotion detection
- Crowd simulation
- Event based Dynamic Bayesian Network
- Instantaneous Topological Map
- Log-likelihood ratio
Cite this
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Perception of emotions from crowd dynamics. / Baig, M.W.; Baig, M.S.; Bastani, V.; Barakova, Emilia I.; Marcenaro, L.; Regazzoni, C.S.; Rauterberg, Matthias.
2015 IEEE International Conference on Digital Signal Processing, DSP 2015, 21-24 July 2015, Singapore. Piscataway : Institute of Electrical and Electronics Engineers, 2015. p. 703-707 7251966.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Perception of emotions from crowd dynamics
AU - Baig, M.W.
AU - Baig, M.S.
AU - Bastani, V.
AU - Barakova, Emilia I.
AU - Marcenaro, L.
AU - Regazzoni, C.S.
AU - Rauterberg, Matthias
PY - 2015/9/9
Y1 - 2015/9/9
N2 - Perceiving crowd emotions and understand the situation is vital to control the situations in surveillance applications. This paper introduces the evolution of methods for crowd emotion perception based on bio-inspired probabilistic models. The emotions have been perceived both in an offline and online manner from the crowd. We focus on the perception of emotion from crowd behavior and dynamics. The paper explains few probabilistic algorithms and compares these for detection of emotion of crowds and proposes a probabilistic modelling approach which is trained on data to perceive the emotions of the crowd in an area under surveillance. Emotions are defined as evolving dynamic patterns arising due to interaction of people in an environment with their relationships to the past interaction patterns. Camera sensors are used to track the motion of the individuals within a crowd scenario under observation. The data mining techniques are used to distinguish between different behaviors and events into positive and negative emotions. The results have been evaluated using simulated data from a proposed office environment.
AB - Perceiving crowd emotions and understand the situation is vital to control the situations in surveillance applications. This paper introduces the evolution of methods for crowd emotion perception based on bio-inspired probabilistic models. The emotions have been perceived both in an offline and online manner from the crowd. We focus on the perception of emotion from crowd behavior and dynamics. The paper explains few probabilistic algorithms and compares these for detection of emotion of crowds and proposes a probabilistic modelling approach which is trained on data to perceive the emotions of the crowd in an area under surveillance. Emotions are defined as evolving dynamic patterns arising due to interaction of people in an environment with their relationships to the past interaction patterns. Camera sensors are used to track the motion of the individuals within a crowd scenario under observation. The data mining techniques are used to distinguish between different behaviors and events into positive and negative emotions. The results have been evaluated using simulated data from a proposed office environment.
KW - Autobiographical Memory
KW - Crowd emotion detection
KW - Crowd simulation
KW - Event based Dynamic Bayesian Network
KW - Instantaneous Topological Map
KW - Log-likelihood ratio
UR - http://www.scopus.com/inward/record.url?scp=84961366951&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2015.7251966
DO - 10.1109/ICDSP.2015.7251966
M3 - Conference contribution
AN - SCOPUS:84961366951
SP - 703
EP - 707
BT - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015, 21-24 July 2015, Singapore
PB - Institute of Electrical and Electronics Engineers
CY - Piscataway
ER -