A Psychologically Inspired Fuzzy Cognitive Deep Learning Framework to Predict Crowd Behavior
- Resource Type
- Authors
- Stefano Berretti; Sabu M. Thampi; Elizabeth B. Varghese
- Source
- IEEE Transactions on Affective Computing. 13:1005-1022
- Subject
- Exploit
business.industry
Computer science
Deep learning
media_common.quotation_subject
Cognition
02 engineering and technology
Ambiguity
Machine learning
computer.software_genre
Fuzzy logic
Visualization
Human-Computer Interaction
Data_GENERAL
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Personality
020201 artificial intelligence & image processing
Artificial intelligence
business
Crowd psychology
computer
Software
media_common
- Language
- ISSN
- 2371-9850
In an intelligent surveillance system, detecting and predicting diverse collective crowd behaviors has emerged as a challenging problem for efficient crowd management. In real-world scenarios, potential disasters and hazards can be averted by considering crowd psychology for predicting crowd behaviors. This paper proposes an approach that exploits the psychological and cognitive aspects of human behavior in determining nine diverse crowd behaviors. The proposed approach is a combination of two cognitive deep learning frameworks and a psychological fuzzy computational model that utilizes OCC theory of emotions, OCEAN five-factor model of personality and visual attention for detecting crowd behaviors. Experiments are performed on different datasets and the results prove that our approach is successful in detecting and predicting crowd behavior in confronting situations and also outperforms the state-of-the-art methods. In particular, considering psychological aspects and cognition in determining crowd behavior is beneficial for rectifying the semantic ambiguity in identifying crowd behaviors.