A bio-inspired logical process for saliency detections in cognitive crowd monitoring

It 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

Bio-inspired relevant interaction modelling in cognitive crowd management

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

INFORMATION BOTTLENECK-BASED RELEVANT KNOWLEDGE REPRESENTATION IN LARGE-SCALE VIDEO SURVEILLANCE SYSTEMS

In 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

In 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

Run Length Encoded Dynamic Bayesian Networks for Probabilistic Interaction Modeling

Human 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

A switching fusion filter for dim point target tracking in infra-red video sequences

Motion 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

Event definition for stability preservation in bio-inspired cognitive crowd monitoring

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 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 track-before-detect algorithm using joint probabilistic data association filter and interacting multiple models

Detection 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

A multi-sensor cognitive approach for active security monitoring of abnormal overcrowding situations

Abstract\u2014Intelligent camera networks have been lately employed for a wide range of heterogeneous purposes, concerning both security and safety oriented systems. Military and civil applications ranging from border surveillance and public spaces monitoring to ambient intelligence and road safety are representative of such various applications. In this paper a discussion on the exploitation of a cognitive-based architecture, coupling simulation tools to real scenarios for interaction modelling and analysis, is presented. The application of the proposed general framework, which is given the name of Cognitive Node - CN, to crowd monitoring is hereby presented

Diagnostic performance of a Lattice Boltzmann-based method for CT-based fractional flow reserve

AIMS: Fractional flow reserve (FFR) estimated from coronary computed tomography angiography (CT-FFR) offers non-invasive detection of lesion-specific ischaemia. We aimed to develop and validate a fast CT-FFR algorithm utilising the Lattice Boltzmann method for blood flow simulation (LBM CT-FFR). METHODS AND RESULTS: Sixty-four patients with clinically indicated CTA and invasive FFR measurement from three institutions were retrospectively analysed. CT-FFR was performed using an onsite tool interfacing with a commercial Lattice Boltzmann fluid dynamics cloud-based platform. Diagnostic accuracy of LBM CT-FFR ≤0.8 and percent diameter stenosis >50% by CTA to detect invasive FFR ≤0.8 were compared using area under the receiver operating characteristic curve (AUC). Sixty patients successfully underwent LBM CT-FFR analysis; 29 of 73 lesions in 69 vessels had invasive FFR ≤0.8. Total time to perform LBM CT-FFR was 40±10 min. Compared to invasive FFR, LBM CT-FFR had good correlation (r=0.64), small bias (0.009) and good limits of agreement (-0.223 to 0.206). The AUC of LBM CT-FFR (AUC=0.894, 95% confidence interval [CI]: 0.792-0.996) was significantly higher than CTA (AUC=0.685, 95% CI: 0.576-0.794) to detect FFR ≤0.8 (p=0.0021). Per-lesion specificity, sensitivity, and accuracy of LBM CT-FFR were 97.7%, 79.3%, and 90.4%, respectively. CONCLUSIONS: LBM CT-FFR has very good diagnostic accuracy to detect lesion-specific ischaemia (FFR ≤0.8) and can be performed in less than one hour