1,720,965 research outputs found
Run Length Encoded Dynamic Bayesian Networks for Probabilistic Interaction Modeling
No full textHuman behavior analysis for Cognitive Surveillance Systems (CSS) share mainly the concept that it can be time to extend functionalities beyond simple video analytics. In most recent systems addressed by research, automatic support to human decisions based on object detection, tracking and situation assessment tools is integrated as a part of a complete cognitive artificial process. In such cases a CSS needs to represent complex situations that describe alternative possible real time interactions between the dynamic observed situation and operators’ actions. To obtain such knowledge, particular types of Event based Dynamic Bayesian Networks E-DBNs are here proposed. In this paper it is shown how, by means of Run Length Encoding (RLE) of off line acquired information, the cognitive system is able to represent and anticipate possible operators’ actions within the CSS. Results are shown by considering a crowd monitoring application in a critical infrastructure. A system is presented where a CSS embedding in a structured way RLE E-DBN knowledge can interact with an active visual simulator of crowd situations. Outputs from such a simulator can be easily compared with video signals coming from real cameras and processed by typical Bayesian tracking methods
Simulation based learning of operator-environment interactions for dynamic crowd monitoring by means of a Bayesian Dynamical Switching Model
No full textA switching fusion filter for dim point target tracking in infra-red video sequences
No full textMotion and depth perceptions allow, to the human visual system, of interpreting the object movements by surrounding environmental information processing. The cognitive science applied to computer vision field can be considered an important innovation in order to increase the detection and tracking performances. These tasks play a fundamental role for detecting and tracking of dim moving point targets in Infra-Red (IR) images, which are characterized by low levels of SNR. In such cases, by means of the paradigm of Track-Before-Detect (TBD) based detection algorithm, it is possible to distinguish the target from image background. This paper presents an innovative TBD based approach relies on interacting multiple target models, which is called Fusion Filters (FFs), for far objects in IR sequences. Specifically, through two different Kalman filters it is possible to estimate separately position and dimension of the target. By means of switching probabilistic models, the proposed framework infers on the different target motion percepts. Such a process permits to obtain the global state of the object by merging position with size estimates. The experimental results on real and simulated sequences demonstrate the effectiveness of the proposed approach
A bio-inspired knowledge representation method for anomaly detection in cognitive video surveillance systems
No full textHuman behaviour analysis has important applications
in the field of anomaly management, such as Intelligent
Video Surveillance (IVS). As the number of individuals in a scene
increases, however, new macroscopic complex behaviours emerge
from the underlying interaction network among multiple agents.
This phenomenon has lately been investigated by modelling such
interaction through Social Forces.
In most recent Intelligent Video Surveillance systems, mechanisms
to support human decisions are integrated in cognitive
artificial processes. These algorithms mainly address the problem
of modelling behaviours to allow for inference and prediction
over the environment. 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 (i.e. the crowd). Such models are
stored within a 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 bioinspired
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, where crowd behaviour is modelled by
means of Social Forces and can interact with a human operator
within a visual 3D simulator. The way anomalies are detected and
consequently handled is demonstrated on synthetic data and also
on a real video sequence, in both the user-support and automatic
modes
Information Bottleneck-based relevant knowledge representation in large-scale video surveillance systems
No full textIn the large-scale video surveillance systems relevant information
extraction and representation processes play an important role
in the interpretation of the scenes. In particular, when the amount
information grows up, due to a large number of monitored areas, it
could be necessary to focus the attention on a part of total available
information only. In this cases, one of the main problems in event
detections is to reconstruct the scene from limited observations. In
this paper an innovative way of sparse information representation,
based on information theory, is presented. The Self Organizing Maps
(SOMs) have been employed at two different steps: for classifying
and correlating observed sparse data time series. By means of Information
Bottleneck it is possible to determine the best data representation
(in the SOM-space) as trade-off between the capabilities to
recover the signals and maintain the statistical similarities of original
data. The experiments shown how the so called information bottleneck
based SOM selection, for knowledge modelling, can be applied
to the field of crowd monitoring for people density map estimation
and event detection. The results on synthetic and also on real video
sequences are presented
Selective attention automatic focus for cognitive crowd monitoring
No full textIn most recent Intelligent Video Surveillance systems,
mechanisms used to support human decisions are integrated
in cognitive artificial processes. Large scale video surveillance
networks must be able to analyse a huge amount of
information. In this context, a cognitive perception mechanism
integrate in an intelligent system could help an operator
for focusing his attention on relevant aspects of the
environment ignoring other parts. This paper presents a
bio-inspired algorithm called Selective Attention Automatic
Focus (S2AF), as a part of more complex Cognitive Dynamic
Surveillance System (CDSS) for crowd monitoring.
The main objective of the proposed method is to extract
relevant information needed for crowd monitoring directly
from the environmental observations. Experimental results
are provided by means of a 3D crowd simulator; they show
how by the proposed attention focus method is able to detect
densely populated areas
A track-before-detect algorithm using joint probabilistic data association filter and interacting multiple models
No full textDetection of dim moving point targets in cluttered background can have a great impact on the tracking performances. This may become a crucial problem, especially in low-SNR environments, where target characteristics are highly susceptible to corruption. In this paper, an extended target model, namely Interacting Multiple Model (IMM), applied to Track-Before-Detect (TBD) based detection algorithm, for far objects, in infrared (IR) sequences is presented. The approach can automatically adapts the kinematic parameter estimations, such as position and velocity, in accordance with the predictions as dimensions of the target change. A sub-par sensor can cause tracking problems. In particular, for a single object, noisy observations (i.e. fragmented measures) could be associated to different tracks. In order to avoid this problem, presented framework introduces a cooperative mechanism between Joint Probabilistic Data Association Filter (JPDAF) and IMM. The experimental results on real and simulated sequences demonstrate effectiveness of the proposed approach
Bio-inspired relevant interaction modelling in cognitive crowd management
Full text linkCognitive 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-eect relationships between user actions and changes
in environment congurations. Such models are stored within a memory (Autobiographical
Memory) during a learning phase. Here the system operates
an eective 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 knowledgetransfer
phase, learned representations can be used, at dierent 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,
Department of Naval, Electric, Electronic and Telecommunications Engineering, University
of Genoa
Via Opera Pia 11A, 16100, Genoa, Italy
Tel.: +39-010-3532212
Fax: +39-010-3532134
e-mail: (see http://www.isip40.it)
2 Simone Chiappino et al. on synthetic and also on real video sequences, in both the user-support and automatic modes
Event definition for stability preservation in bio-inspired cognitive crowd monitoring
No full textIn most recent Intelligent Video Surveillance systems,
mechanisms to support human decisions are integrated in
cognitive artificial processes. These algorithms mainly address
the problem of extraction and modelling of relevant
information from a sensor network. In crowd monitoring
the main problem is to individuate specific events as for example
different behaviours among interacting entities. A
bio-inspired structure for modelling cause-effect relationships
between events was lately proposed by the authors and
applied to the field of automatic crowd monitoring. Such
cause-effect relationships are modelled by means of coupled
Event-based Dynamic Bayesian Networks and stored within
an Autobiographical Memory during a learning phase, in order
to supply appropriate knowledge to the automatic system
in the on-line phase. However, the definition of causality
relies on the selection of relevant events, which is performed
by means of Self Organizing Maps and on a temporal scale
defined by a newly introduced temporal parameter. Performances
of the proposed multi-camera video surveillance
system are studied on tuning such causality parameters
A bio-inspired logical process for saliency detections in cognitive crowd monitoring
No full textIt is well known from physiological studies that the level of human attention for adult individuals rapidly decreases after five to twenty minutes [1]. Attention retention for a surveillance operator represents a crucial aspect in Video Surveillance applications and could have a significant impact in identifying relevance, especially in crowded situations. In this field, advanced mechanisms for selection and extraction of saliency information can improve the performances of autonomous video surveillance systems and increase the effectiveness of human operator support. In particular, crowd monitoring represents a central aspect in many practical applications for managing and preventing emergencies due to panic and overcrowding
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