Determining the best suited semantic events for cognitive surveillance

Fernández, Carles; Baiget, Pau; Roca, Francesc; Gonzílez, Jordi

doi:10.1016/j.eswa.2010.09.070

Cited by 20 publications

(5 citation statements)

References 26 publications

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“…Besides the role of fuzzy logic in objects detection/tracking and traffic surveillance, there are other domains of surveillance where these concepts and implementations are applied. These domains include diverse applications such as healthcare [76,77] particularly for elderly people [34], fusion of images (infrared and visible regions) [35], and events determination [40]. Further research contributions in the surveillance domain include static and moving objects in surveillance [44], activity modeling [9], video enhancement [46,78], risk assessment [47], and walk directions estimations [52,79].…”

Section: Fuzzy Logic Based Methods For Surveillance Bvdmentioning

confidence: 99%

“…The commonly utilized metrics in computer vision domain including precision, recall, F1-score, accuracy, false positives and negatives, peak signal to noise ratio, accuracy rate, and false detection rate, among others, are also employed by some existing algorithms. For instance, the authors in [38,40,41], implemented accuracy, precision, recall, and F1 measures to justify their output results in various surveillance domains, including human detection and their motion information computation, as given in with these methods, it is possible to evaluate and validate the performance of fuzzy logic in surveillance domains when compared to other approaches employing machine learning and traditional image processing techniques.…”

Section: Performance Evaluation Of Fuzzy Based Surveillance Bvd Analyticsmentioning

confidence: 99%

“…Fuzzy metric temporal logic rules dictionary for guiding the operator's attention in surveillance video and automatic report generation Simulation results [40] An ontology-based method for identification, modeling, and generalization of the most relevant events to a specific domain Semantic events detection Top-down general knowledge is built based on enduser descriptions of events, followed by automatic video indexing via bottom-up approach.…”

Section: Object Detection With Dynamic Backgroundsmentioning

confidence: 99%

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Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term Big Video Data (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real-world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this article, we draw researchers’ attention toward the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook toward future research directions derived from our critical assessment of the efforts invested so far in this exciting field.

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Section: Fuzzy Logic Based Methods For Surveillance Bvdmentioning

confidence: 99%

Section: Performance Evaluation Of Fuzzy Based Surveillance Bvd Analyticsmentioning

confidence: 99%

Section: Object Detection With Dynamic Backgroundsmentioning

confidence: 99%

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Fuzzy Logic in Surveillance Big Video Data Analysis

et al. 2021

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“…-Monitoring Communications: is usually achieved by conducting an "intrusive surveillance" [27,28] (i.e covert vehicle [29], mainly a covert spying van), conducting a "directed surveillance" [30] (i.e relying on smart street or security cameras), or using human agents [31]. -Intercepting Communications: is usually achieved by identifying and intercepting IP/MAC addresses of cyber-criminals and suspects alike [32,33], in addition to tracking their e-mails/web-activities, and identifying their phone numbers and monitoring their phones messages, calls and logs (i.e land-lines and smart-phones [34]).…”

Section: Forensics Chain Of Custodymentioning

confidence: 99%

Digital Forensics vs. Anti-Digital Forensics: Techniques, Limitations and Recommendations

Yaacoub,

Noura,

Salman

et al. 2021

Preprint

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The number of cyber attacks has increased tremendously in the last few years. This resulted into both human and financial losses at the individual and organization levels. Recently, cyber-criminals are leveraging new skills and capabilities by employing anti-forensics activities, techniques and tools to cover their tracks and evade any possible detection. Consequently, cyber-attacks are becoming more efficient and more sophisticated. Therefore, traditional cryptographic and non-cryptographic solutions and access control systems are no longer enough to prevent such cyber attacks, especially in terms of acquiring evidence for attack investigation. Hence, the need for well-defined, sophisticated, and advanced forensics investigation tools are highly required to track down cyber criminals and to reduce the number of cyber crimes. This paper reviews the different forensics and anti-forensics methods, tools, techniques, types, and challenges, while also discussing the rise of the anti-anti-forensics as a new forensics protection mechanism against anti-forensics activities. This would help forensics investigators to better understand the different anti-forensics tools, methods and techniques that cyber criminals employ while launching their attacks. Moreover, the limitations of the current forensics techniques are discussed, especially in terms of issues and challenges. Finally, this paper presents a holistic view from a literature point of view over the forensics domain and also helps other fellow colleagues in their quest to further understand the digital forensics domain.

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“…The authors classify events into four types: primitive, action, interaction, and composite. Fernández, Baiget, Roca, and González (2011) present an ontology-based methodology that guides the identification, stepby-step modeling, and generalization of the most relevant events to a specific surveillance domain. Gómez-Romero, Patricio, García, and Molina (2011) propose a computer vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information.…”

Section: Introductionmentioning

confidence: 99%

Human activity monitoring by local and global finite state machines

Fernández-Caballero

Castillo

Rodríguez-Sánchez

2012

Expert Systems with Applications

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There are a number of solutions to automate the monotonous task of looking at a monitor to find suspicious behaviors in video surveillance scenarios. Detecting strange objects and intruders, or tracking people and objects, is essential for surveillance and safety in crowded environments. The present work deals with the idea of jointly modeling simple and complex behaviors to report local and global human activities in natural scenes. Modeling human activities with state machines is still common in our days and is the approach offered in this paper. We incorporate knowledge about the problem domain into an expected structure of the activity model. Motion-based image features are linked explicitly to a symbolic notion of hierarchical activity through several layers of more abstract activity descriptions. Atomic actions are detected at a low level and fed to hand-crafted grammars to detect activity patterns of interest. Also, we work with shape and trajectory to indicate the events related to moving objects. In order to validate our proposal we have performed several tests with some CAVIAR test cases.

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Determining the best suited semantic events for cognitive surveillance

Cited by 20 publications

References 26 publications

Fuzzy Logic in Surveillance Big Video Data Analysis

Fuzzy Logic in Surveillance Big Video Data Analysis

Digital Forensics vs. Anti-Digital Forensics: Techniques, Limitations and Recommendations

Human activity monitoring by local and global finite state machines

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