The Experts below are selected from a list of 16689 Experts worldwide ranked by ideXlab platform
Walter Stechele - One of the best experts on this subject based on the ideXlab platform.
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Default Reasoning for Forensic Visual Surveillance Based on Subjective Logic and its Comparison with L-Fuzzy Set Based Approaches
Multimedia Data Engineering Applications and Processing, 2013Co-Authors: Seunghan Han, Walter StecheleAbstract:Default reasoning can provide a means of deriving plausible semantic conclusion under imprecise and contradictory information in forensic Visual Surveillance. In such reasoning under uncertainty, proper uncertainty handling formalism is required. A discrete species of Bilattice for multivalued default logic demonstrated default reasoning in Visual Surveillance. In this article, the authors present an approach to default reasoning using subjective logic that acts in a continuous space. As an uncertainty representation and handling formalism, subjective logic bridges Dempster Shafer belief theory and second order Bayesian, thereby making it attractive tool for artificial reasoning. For the verification of the proposed approach, the authors extend the inference scheme on the bilattice for multivalued default logic to L-fuzzy set based logics that can be modeled with continuous species of bilattice structures. The authors present some illustrative case studies in Visual Surveillance scenarios to contrast the proposed approach with L-fuzzy set based approaches.
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ICME - A reasoning approach to enable abductive semantic explanation upon collected observations for forensic Visual Surveillance
2011 IEEE International Conference on Multimedia and Expo, 2011Co-Authors: Seunghan Han, Andreas Hutter, Walter StecheleAbstract:This paper proposes an approach to enable automatic generation of probable semantic hypotheses for a given set of collected observations for forensic Visual Surveillance. As video analytic power exploited in Visual Surveillance is getting matured, the more automatically generated intermediate semantic metadata became available. In the sense of forensic reuse of such data, the majority of approaches have been focused on specific semantic query based scene analysis. However, in reality, there are often cases in which it is more natural to reason about the most probable semantic explanation of a scene given a collection of specific semantic evidences. In general, this type of diagnostic reasoning is known as abduction. To enable such a semantic reasoning, in this paper, we propose a layered reasoning pipeline that combines abductive logic programming together with backward and forward chaining based deductive logic programming. To rate derived hypotheses, we apply subjective logic. We present a conceptual case study in a distributed camera based scenario. The case study shows the potential and feasibility of the proposed approach for forensic analysis of Visual Surveillance data.
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ICSC - Subjective Logic Based Approach to Modeling Default Reasoning for Visual Surveillance
2010 IEEE Fourth International Conference on Semantic Computing, 2010Co-Authors: Seunghan Han, Bonjung Koo, Walter StecheleAbstract:In forensic analysis of Visual Surveillance data, ‘default reasoning’ can play an important role for deriving plausible semantic conclusions under incomplete and contradictory information about scenes. In this paper, we present an inference framework for default reasoning using Subjective Logic theory. Subjective Logic is a relatively new branch of probabilistic logic that allows explicit representation of ignorance about knowledge in a model called subjective opinion and that also comes with a rich set of operators thereby, having big potential as a tool for belief representation and reasoning. However, its application to Visual Surveillance is in its infancy and its use for default reasoning is not reported yet. Therefore, the aim of this paper is to bestow the ability of default reasoning on subjective logic and show the feasibility of using the introduced inference framework for Visual Surveillance. Among the approaches to enable default reasoning, the Bilattice framework is one that is well known and demonstrated for Visual Surveillance. For deriving the usage of subjective logic for default reasoning, we first discuss the similarity between the partial ignorance concept in subjective logic and the concept of degree of information in Bilattice based structure for multivalued default logic. Then we introduce the inference mechanism for default reasoning by mapping multi-logic-values into subjective opinion and combining operators in subjective logic. Finally, we present some illustrative reasoning examples in typical Visual Surveillance scenarios.
Seunghan Han - One of the best experts on this subject based on the ideXlab platform.
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Default Reasoning for Forensic Visual Surveillance Based on Subjective Logic and its Comparison with L-Fuzzy Set Based Approaches
Multimedia Data Engineering Applications and Processing, 2013Co-Authors: Seunghan Han, Walter StecheleAbstract:Default reasoning can provide a means of deriving plausible semantic conclusion under imprecise and contradictory information in forensic Visual Surveillance. In such reasoning under uncertainty, proper uncertainty handling formalism is required. A discrete species of Bilattice for multivalued default logic demonstrated default reasoning in Visual Surveillance. In this article, the authors present an approach to default reasoning using subjective logic that acts in a continuous space. As an uncertainty representation and handling formalism, subjective logic bridges Dempster Shafer belief theory and second order Bayesian, thereby making it attractive tool for artificial reasoning. For the verification of the proposed approach, the authors extend the inference scheme on the bilattice for multivalued default logic to L-fuzzy set based logics that can be modeled with continuous species of bilattice structures. The authors present some illustrative case studies in Visual Surveillance scenarios to contrast the proposed approach with L-fuzzy set based approaches.
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ICME - A reasoning approach to enable abductive semantic explanation upon collected observations for forensic Visual Surveillance
2011 IEEE International Conference on Multimedia and Expo, 2011Co-Authors: Seunghan Han, Andreas Hutter, Walter StecheleAbstract:This paper proposes an approach to enable automatic generation of probable semantic hypotheses for a given set of collected observations for forensic Visual Surveillance. As video analytic power exploited in Visual Surveillance is getting matured, the more automatically generated intermediate semantic metadata became available. In the sense of forensic reuse of such data, the majority of approaches have been focused on specific semantic query based scene analysis. However, in reality, there are often cases in which it is more natural to reason about the most probable semantic explanation of a scene given a collection of specific semantic evidences. In general, this type of diagnostic reasoning is known as abduction. To enable such a semantic reasoning, in this paper, we propose a layered reasoning pipeline that combines abductive logic programming together with backward and forward chaining based deductive logic programming. To rate derived hypotheses, we apply subjective logic. We present a conceptual case study in a distributed camera based scenario. The case study shows the potential and feasibility of the proposed approach for forensic analysis of Visual Surveillance data.
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ICSC - Subjective Logic Based Approach to Modeling Default Reasoning for Visual Surveillance
2010 IEEE Fourth International Conference on Semantic Computing, 2010Co-Authors: Seunghan Han, Bonjung Koo, Walter StecheleAbstract:In forensic analysis of Visual Surveillance data, ‘default reasoning’ can play an important role for deriving plausible semantic conclusions under incomplete and contradictory information about scenes. In this paper, we present an inference framework for default reasoning using Subjective Logic theory. Subjective Logic is a relatively new branch of probabilistic logic that allows explicit representation of ignorance about knowledge in a model called subjective opinion and that also comes with a rich set of operators thereby, having big potential as a tool for belief representation and reasoning. However, its application to Visual Surveillance is in its infancy and its use for default reasoning is not reported yet. Therefore, the aim of this paper is to bestow the ability of default reasoning on subjective logic and show the feasibility of using the introduced inference framework for Visual Surveillance. Among the approaches to enable default reasoning, the Bilattice framework is one that is well known and demonstrated for Visual Surveillance. For deriving the usage of subjective logic for default reasoning, we first discuss the similarity between the partial ignorance concept in subjective logic and the concept of degree of information in Bilattice based structure for multivalued default logic. Then we introduce the inference mechanism for default reasoning by mapping multi-logic-values into subjective opinion and combining operators in subjective logic. Finally, we present some illustrative reasoning examples in typical Visual Surveillance scenarios.
Jie Cao - One of the best experts on this subject based on the ideXlab platform.
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real time target detection and recognition with deep convolutional networks for intelligent Visual Surveillance
IEEE ACM International Conference Utility and Cloud Computing, 2016Co-Authors: Hao Lan Zhang, Bo Mao, Jie CaoAbstract:Moving target detection and tracking, recognition, behaviors analysis are the key issues in the intelligent Visual Surveillance system (IVSS). The challenge is how to process the real-time video stream in an effective way in case that we could find the interested objects for analysis. However, the traditional video Surveillance technology often does not meet the needs of real-time key frame recognition for the on-line intelligent video monitoring system. In our paper, we adopt the state-of-the-art Faster R-CNN [7] that takes advantages of convolutional neural networks into our real-time target recognition system - Deep Intelligent Visual Surveillance (DIVS). The key aspects of our DIVS are consisted of four parts: (i) Getting the real-time video images from remote cameras; (ii) Processing the data with the deep learning framework caffe [23] built for Faster R-CNN; (iii) Storing the valuable data with MySQL; (iv) Data presentation on the website. Experiments based on our system validated the effectiveness, stability and accuracy of our proposed solutions.
Stephen J Maybank - One of the best experts on this subject based on the ideXlab platform.
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a survey on Visual Surveillance of object motion and behaviors
Systems Man and Cybernetics, 2004Co-Authors: Tieniu Tan, Liang Wang, Stephen J MaybankAbstract:Visual Surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive Surveillance using multiple cameras, etc. In general, the processing framework of Visual Surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of Surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote Surveillance.
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the advisor Visual Surveillance system
2004Co-Authors: Nils T Siebel, Stephen J MaybankAbstract:ADVISOR is an automated Visual Surveillance system for metro stations which was developed as part of the project ADVISOR, involving 3 academic and 3 industrial project partners. The ADVISOR system aims at making public transport safer by automatically detecting at an early stage dangerous situations which may lead to accidents, violence or vandalism. In order to achieve this people are tracked across the station and their behaviours analysed. Additional measurements on crowd density and movement are also obtained. Warnings are generated and displayed to human operators for possible intervention. The article explores the main difficulties encountered during the design and implementation of ADVISOR and describes the ways in which they were solved. A prototype system has been built and extensively tested, proving the feasibility of automated Visual Surveillance systems. An analysis of test runs at a metro station in Barcelona and several individual experiments show that the system copes with many difficult image analysis problems. The analysis also points the way for future development and ways of deployment of the techniques used in the system.
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Automatic Visual Surveillance of Vehicles and People
Advanced Video-Based Surveillance Systems, 1999Co-Authors: Paolo Remagnino, Stephen J Maybank, R. Fraile, Keith D. Baker, R. MorrisAbstract:The last decade has seen a large increase in the use of Visual Surveillance cameras. These are often installed in concourses, car park areas and high security sites to monitor the flow of pedestrians and vehicles for security and data analysis. The job of monitoring image sequences is usually assigned to a human operator who waits for important events to occur. Operators rapidly become bored and lose concentration. It is therefore essential to devise autonomous Surveillance systems, which can interpret the images and alert a human operator only when suspicious events occur.
Dimitrios Makris - One of the best experts on this subject based on the ideXlab platform.
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Intelligent Visual Surveillance: Towards Cognitive Vision Systems
The Open Cybernetics & Systemics Journal, 2008Co-Authors: Dimitrios Makris, Tim Ellis, J BlackAbstract:Automated Visual Surveillance systems are required to emulate the cognitive abilities of Surveillance personnel, who are able to detect, recognise and assess the severity of suspicious, unusual and threatening behaviours. We describe the architecture of our Surveillance system, emphasising some of its high-level cognitive capabilities. In particular, we present a methodology for automatically learning semantic labels of scene features and automatic detection of atypical events. We also describe a framework that supports learning of a wider range of semantics, using a motion attention mechanism and exploiting long-term consistencies in video data. Visual Surveillance systems are widely used in public places. Traditional Surveillance systems consist of cameras, storage devices, video monitors and security personnel. Se- curity staff monitor the activity in the scene, watching for suspicious or threatening activities. In addition to online monitoring, post-examination of recorded video data may be required to identify suspicious persons, vehicles or events. Both tasks are tedious, as security staff need to identify spe- cific and unusual events from a large number of very com- mon and repetitive events. Unfortunately, human operators usually struggle to deal with the required huge cognitive overload, even for a small Surveillance system of few cam- eras. Current commercial Surveillance systems make use of digital technology to capture, store and process video data. For example, Video Motion Detectors (VMDs) are able to automatically detect scene motion and send a notification signal to an operator. However, their operation is still primi- tive and not sufficiently discriminatory (e.g. in busy envi- ronments, motion is continuously detected). In general, Visual Surveillance systems are required to minimise the role of human operators. More specifically, some of the requirements are automatic detection of suspi- cious events that eases online monitoring, and context-based databases of the observed events that facilitate conceptual querying and searching. Cognitive vision systems that evolve and adapt to their environments could potentially fulfil such requirements. These systems are expected to outperform the human operators, as they will be able to operate reliably and continuously, without the fatigue constraint.
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CVPR - Object Classification in Visual Surveillance Using Adaboost
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007Co-Authors: J.-p. Renno, Dimitrios Makris, G.a. JonesAbstract:In this paper, we present a method of object classification within the context of Visual Surveillance. Our goal is the classification of tracked objects into one of the two classes: people and cars. Using training data comprised of trajectories tracked from our car-park, a weighted ensemble of Adaboost classifiers is developed. Each ensemble is representative of a particular feature, evaluated and normalised by its significance. Classification is performed using the sub-optimal hyper-plane derived by selection of the N-best performing feature ensembles. The resulting performance is compared to a similar Adaboost classifier, trained using a single ensemble over all dimensions.
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Learning semantic scene models from observing activity in Visual Surveillance
IEEE transactions on systems man and cybernetics. Part B Cybernetics : a publication of the IEEE Systems Man and Cybernetics Society, 2005Co-Authors: Dimitrios Makris, Tim EllisAbstract:This paper considers the problem of automatically learning an activity-based semantic scene model from a stream of video data. A scene model is proposed that labels regions according to an identifiable activity in each region, such as entry/exit zones, junctions, paths, and stop zones. We present several unsupervised methods that learn these scene elements and present results that show the efficiency of our approach. Finally, we describe how the models can be used to support the interpretation of moving objects in a Visual Surveillance environment.
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a hierarchical database for Visual Surveillance applications
International Conference on Multimedia and Expo, 2004Co-Authors: J Black, Tim Ellis, Dimitrios MakrisAbstract:This paper presents a framework for event detection and video content analysis for Visual Surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a Surveillance region, and have defined data models to support the storage of different layers of abstraction of tracking data into a Surveillance database. The Surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire Surveillance region in terms of the semantic scene model. In addition, the Surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications
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ICME - A hierarchical database for Visual Surveillance applications
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004Co-Authors: J Black, Tim Ellis, Dimitrios MakrisAbstract:This paper presents a framework for event detection and video content analysis for Visual Surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a Surveillance region, and have defined data models to support the storage of different layers of abstraction of tracking data into a Surveillance database. The Surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire Surveillance region in terms of the semantic scene model. In addition, the Surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications
