Structured Context-Analysis Techniques in Biologically Inspired Ambient-Intelligence Systems

Marchesotti, Luca; Piva, S.; Regazzoni, Carlo S.

doi:10.1109/tsmca.2004.838483

Cited by 16 publications

(11 citation statements)

References 14 publications

(18 reference statements)

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“…These facts will become clearer in Sect. 4.1 A technique for the definition of contextual knowledge was prosed in (Marchesotti et al 2005). By using a single 2-D SOM, an event classifications was obtained by fusing of the heterogeneous vectors, shown in Eq.…”

Section: Event Detectionmentioning

confidence: 99%

Bio-inspired relevant interaction modelling in cognitive crowd management

Chiappino

Morerio

Regazzoni

2014

J Ambient Intell Human Comput

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Cognitive algorithms, integrated in intelligent systems, represent an important innovation in designing interactive smart environments. More in details, Cognitive Systems have important applications in anomaly detection and management in advanced video surveillance. These algorithms mainly address the problem of modelling interactions and behaviours among the main entities in a scene. A bio-inspired structure is here proposed, which is able to encode and synthesize signals, not only for the description of single entities behaviours, but also for modelling cause-effect relationships between user actions and changes in environment configurations. Such models are stored within a memory (Autobiographical Memory) during a learning phase. Here the system operates an effective knowledge transfer from a human operator towards an automatic systems called Cognitive Surveillance Node (CSN), which is part of a complex cognitive JDL-based and bio-inspired architecture. After such a knowledge-transfer phase, learned representations can be used, at different levels, either to support human decisions, by detecting anomalous interaction models and thus compensating for human shortcomings, or, in an automatic decision scenario, to identify anomalous patterns and choose the best strategy to preserve stability of the entire system. Results are presented in a video surveillance scenario, where the CSN can observe two interacting entities consisting in a simulated crowd and a human operator.These can interact within a visual 3D simulator, where crowd behaviour is modelled by means of Social Forces. The way anomalies are detected and consequently handled is demonstrated, on synthetic and also on real video sequences, in both the user-support and automatic modes.

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Section: Event Detectionmentioning

confidence: 99%

Bio-inspired relevant interaction modelling in cognitive crowd management

Chiappino

Morerio

Regazzoni

2014

J Ambient Intell Human Comput

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“…Ambient Intelligence application usually exploits multiple heterogeneous data sources to understand user behaviour in order to provide him/her with services. In [38] the authors proposed a bio-inspired method to predict user intentions inside a university laboratory by analyzing his position detected by means of a visual tracker and combining this information with parameters (e.g. login status, network bandwidth available, CPU usage, etc.)…”

Section: Logical Architecture Designmentioning

confidence: 99%

“…Advanced surveillance systems combine heterogeneous sensors: static, Pan-Tilt-Zoom (PTZ), Infra-Red (IR), visible or thermal cameras; environmental sensors like Passive Infra-Red (PIR), thermal, fire, seismic, chemical, access control and many other. Environmental sensors are also used for Ambient Intelligence application [38] in order to give services to the users, in this field can be also performed the video radio fusion aiming to a better localization of the users in the area of interest [14]. Video, RADAR and GPS signals can be integrated both to monitor an area like in [43] or for safety scopes for example in driver assistance (e.g.…”

Section: Physical Architecture Designmentioning

confidence: 99%

“…Multi-modal data fusion techniques for situation awareness applications are widely used in the domain of Ambient Intelligence. In [38] multi-modal data are collected in a laboratory to understand the user actions with the aim of allocating them in an efficient way to the available workstations. The exerted sensors are partly hardware devices and partly software routines.…”

Section: Fusion Of Multi-modal Cues For Event Analysismentioning

confidence: 99%

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Multi-Modal Data Fusion Techniques and Applications

Dore¹,

Pinasco²,

Regazzoni³

2009

Multi-Camera Networks

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“…I/E(t) labels was obtained thanks to appropriately trained neural network classifiers based on Self Organizing Maps (S.O.M.) [5]. Training the unsupervised internal map with real-time actual data, 7 significant clusters were identified and labeled with the corresponding I(t) listed in Table 1, as well as 7 E(t) clusters were found by the external SOM classifier and were labeled as in Table 2 (a portion of the trained and labeled SOM map used for internal state is shown in figure 1 as an example).…”

Section: Cause/effect Relationshipsmentioning

confidence: 99%

Inferring Cause/Effect Relationships in Multi-sensor Ambient Intelligence Systems

Piva

Regazzoni

2005

Advances in Visual Computing

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Abstract. In this work a learning technique to provide an Ambient Intelligence (smart space) system with the capacity of predicting variation events in its own internal state is presented. The system and the interacting users are modeled through the instantaneous state vectors obtained as output of two trained Self Organizing Map-based classifiers. The information processed by the system is collected by two sensors sets monitoring several internal and external system variables. Starting from the hypothesis that the user actions have a direct influence on internal system state variables (e.g. work load on personal computers computation or storage devices in a University laboratory, in our current test implementation) we developed a statistical voting algorithm for inferring cause/effect relationships in these instantaneous variations. Logical connections are obtained in unsupervised mode with no a priori information and leads to the definition of a knowledge base the system can exploit to predict its own near future internal state variations, given the observation of the lab users.

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Structured Context-Analysis Techniques in Biologically Inspired Ambient-Intelligence Systems

Cited by 16 publications

References 14 publications

Bio-inspired relevant interaction modelling in cognitive crowd management

Bio-inspired relevant interaction modelling in cognitive crowd management

Multi-Modal Data Fusion Techniques and Applications

Inferring Cause/Effect Relationships in Multi-sensor Ambient Intelligence Systems

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