Event-driven Video Awareness Providing Physical Security

Georgakopoulos, Dimitrios; Baker, Donald G.; Nodine, Marian H.; Cichoki, Andrzej

doi:10.1007/s11280-006-0015-4

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…Integrating other types of EPSs such as those used in video and transactional data analysis (e.g. VEAW [7]) could be one of the interesting applications to explore. We are also planning to refine our approach to accommodate several new functionalities such as allowing event pattern execution to use a combination of slave EPSs and automatic selection of slave EPSs according to the complexity of an event pattern as well as the characteristics of its constituent EPSs.…”

Section: Discussionmentioning

confidence: 99%

A Domain-Driven Approach for Detecting Event Patterns in E-Markets

Mangkorntong

Rabhi

2008

World Wide Web

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An e-market can be thought of as a distributed event system where an event is generated every time the market's state changes in response to a number of human or computing agents. The paper describes a practical application of event processing in an emarket context through conventional Event Processing Systems (EPSs). A new EPS architecture that allows the integration of several existing EPSs under a unified domainspecific user interface and execution environment is proposed. We assess the performance of a prototype system for a case study in financial market surveillance and its ability to provide a common interface for two existing EPSs-SMARTS and Coral8. A discussion on the experimental results and the issues arising from the proposed EPS architecture are also provided.

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Section: Discussionmentioning

confidence: 99%

A Domain-Driven Approach for Detecting Event Patterns in E-Markets

Mangkorntong

Rabhi

2008

World Wide Web

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“…A decentralized event-based system for tracking, analyzing, and controlling electronic document transactions in order to detect suspected insider misuse was presented in [3]. [20] introduces an event-driven architecture for automating video surveillance, orchestrating continuous and tasked video analysis algorithms, merging events from multiple sources, and gathering proactive information to handle missing or incomplete information.…”

Section: Complex Event Processingmentioning

confidence: 99%

Tuning complex event processing rules using the prediction-correction paradigm

Turchin

Gal

Wasserkrug

2009

Proceedings of the Third ACM International Conference on Distributed Event-Based Systems

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There is a growing need for the use of active systems, systems that act automatically based on events. In many cases, providing such active functionality requires materializing (inferring) the occurrence of relevant events. A widespread paradigm for enabling such materialization is Complex Event Processing (CEP), a rule based paradigm, which currently relies on domain experts to fully define the relevant rules. These experts need to provide the set of basic events which serves as input to the rule, their inter-relationships, and the parameters of the events for determining a new event materialization. While it is reasonable to expect that domain experts will be able to provide a partial rules specification, providing all the required details is a hard task, even for domain experts. Moreover, in many active systems, rules may change over time, due to the dynamic nature of the domain. Such changes complicate even further the specification task, as the expert must constantly update the rules. As a result, we seek additional support to the definition of rules, beyond expert opinion. This work presents a mechanism for automating both the initial definition of rules and the update of rules over time. This mechanism combines partial information provided by the domain expert with machine learning techniques, and is aimed at improving the accuracy of event specification and materialization. The proposed mechanism consists of two main repetitive stages, namely rule parameter prediction and rule parameter correction. The former is performed by updating the parameters using an available expert knowledge regarding the future changes of parameters. The latter stage utilizes expert feedback regarding the actual past occurrence of events and the events materialized by the CEP framework to tune rule parameters. We also include possible implementations for both stages, based on a statistical estimator and evaluate our outcome using a case study from the intrusion detection domain.

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“…In recent years, action recognition in surveillance video has drawn considerable attention [8,19,29] from the computer vision research community owing to its potential for public safety. Meanwhile, the application of surveillance cameras is dramatically growing due to the declining cost for semi-conductor components.…”

Section: Introductionmentioning

confidence: 99%

Generalized zero-shot learning for action recognition with web-scale video data

Liu

et al. 2018

World Wide Web

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Action recognition in surveillance video makes our life safer by detecting the criminal events or predicting violent emergencies. However, efficient action recognition is not free of difficulty. First, there are so many action classes in daily life that we cannot pre-define all possible action classes beforehand. Moreover, it is very hard to collect real-word videos for certain particular actions such as steal and street fight due to legal restrictions and privacy protection. These challenges make existing data-driven recognition methods insufficient to attain desired performance. Zero-shot learning is potential to be applied to solve these issues since it can perform classification without positive example. Nevertheless, current zero-shot learning algorithms have been studied under the unreasonable setting where seen classes are absent during the testing phase. Motivated by this, we study the task of action recognition in surveillance video under a more realistic generalized zero-shot setting, where testing data contains both seen and unseen classes. To our best knowledge, this is the first work to study video action recognition under the generalized zero-shot setting. We firstly perform extensive empirical studies on several existing zero-shot leaning approaches under this new setting on a web-scale video data. Our experimental results demonstrate that, under the generalize setting, typical zero-shot learning methods are no longer effective for the dataset we applied. Then, we propose a method for action recognition by deploying generalized zero-shot learning, which transfers the knowledge of web video to detect the anomalous actions in surveillance videos. To verify 2 the effectiveness of our proposed method, we further construct a new surveillance video dataset consisting of nine action classes related to the public safety situation.

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Event-driven Video Awareness Providing Physical Security

Cited by 8 publications

References 9 publications

A Domain-Driven Approach for Detecting Event Patterns in E-Markets

A Domain-Driven Approach for Detecting Event Patterns in E-Markets

Tuning complex event processing rules using the prediction-correction paradigm

Generalized zero-shot learning for action recognition with web-scale video data

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