Interaction Modeling and Prediction in Smart Spaces: A Bio-Inspired Approach Based on Autobiographical Memory

Dore, Alessio; Cattoni, Andréa; Regazzoni, Carlo S.

doi:10.1109/tsmca.2010.2052600

Cited by 19 publications

(11 citation statements)

References 36 publications

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“…To reach this goal, a probable technique is to memorize all possible interactions that occur in related area among people and environment and their reactions due to these actions. A bio-inspired technique is used which has been mentioned in [10][11][12] [13]. These interactions are generated due to causal relationships that happen among persons and environment.…”

Section: Modelling Eventsmentioning

confidence: 99%

Crowd Emotion Detection Using Dynamic Probabilistic Models

Baig

Barakova

Rauterberg

et al. 2014

From Animals to Animats 13

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Abstract. Detecting emotions of a crowd to control the situation is an area of emerging interest. The purpose of this paper is to present a novel idea to detect the emotions of the crowd. Emotions are defined as evolving quantities arising from the reaction to contextual situations in a set of dynamic pattern of events. These events depend on internal and external interaction states in an already mapped space. The emotions of multiple people constituting a crowd in any surveillance environment are estimated by their social and collective behaviors using sensor signals e.g., a camera, which captures and tracks their motion. The feature space is constructed based on local features to model the contextual situations and the different interactions corresponding to different emergent behaviors are modeled using bio-inspired dynamic model. The changes in emotions correspond to behavioral changes which are produced to regulate behaviors under different encountered situations. Proposed algorithm involves the probabilistic signal processing modelling techniques for analysis of different types of collective behaviors based on interactions among people and classification models to estimate emotions as positive or negative. The evaluations are performed on simulated data show the proposed algorithm effectively recognizes the emotions of the crowd under specific situations.

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Section: Modelling Eventsmentioning

confidence: 99%

Crowd Emotion Detection Using Dynamic Probabilistic Models

Baig

Barakova

Rauterberg

et al. 2014

From Animals to Animats 13

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“…(Whitehill & Movellan, 2008) describe a method based on frame by frame head pose tracking robust to illumination changes and different facial appearance. Two classifier trained with the GentleBoost (Friedman et al, 2000) approach are used and the output are integrated using linear regression to estimate pose angles.…”

Section: Related Workmentioning

confidence: 99%

“…In (Dore et al, 2010) has been presented a general framework capable of predicting certain behaviors by studying interaction patterns between humans and the outside world. Such framework takes inspiration from the work of the neurophysiologist A. Damasio (Damasio, 2000).…”

Section: Bio-inspired Model For Interaction Analysismentioning

confidence: 99%

Context Analysis for Situation Assessment in Automotive Applications

Ciardelli¹,

Beoldo²,

Regazzoni³

2011

New Trends and Developments in Automotive Industry

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“…Following this interpretation, in [14] a bio-inspired model is introduced where neurophysiological observations by A. Damasio [15] are the inspiration to define a representation of interactions in a Smart…”

Section: A Modeling Interactions With Dynamic Bayesian Networkmentioning

confidence: 99%

“…Events are then associated to the two subjects and they are distinguished in proto P t and core C t following the terminology of [14].…”

Section: ) State and Observation Definitionmentioning

confidence: 99%

Interaction Analysis with a Bayesian Trajectory Model

Dore

Regazzoni

2010

IEEE Intell. Syst.

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Human behavior recognition is one of the most important and challenging objectives performed by intelligent vision systems. Several issues must be faced in this domain ranging from the extraction of reliable and meaningful features from video streams, to the accurate definition of models able to characterize in a discriminative but, at the same time, general enough way people actions. In this work to cope with these problems a Dynamic Bayesian Network (DBN) model is proposed to recognize people interactions using their patterns of movement. Couples of observed interacting trajectories are processed by the Instantaneous Topological Map (ITM) algorithm to automatically create a topological map used to define the states of the DBN. The trajectories are encoded into the model where conditional probability densities are learned in order to statistically describe interactions. Finally, an online cumulative measure is defined to recognize interactions both in simulated and real-world environments.

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Interaction Modeling and Prediction in Smart Spaces: A Bio-Inspired Approach Based on Autobiographical Memory

Abstract: Abstract-In

Cited by 19 publications

References 36 publications

Crowd Emotion Detection Using Dynamic Probabilistic Models

Crowd Emotion Detection Using Dynamic Probabilistic Models

Context Analysis for Situation Assessment in Automotive Applications

Interaction Analysis with a Bayesian Trajectory Model

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