Subjective Logic Based Approach to Modeling Default Reasoning for Visual Surveillance

2010 ◽  
Author(s):  
Seunghan Han ◽  
Bonjung Koo ◽  
Walter Stechele
Keyword(s):  
Visual Surveillance ◽  
Default Reasoning ◽  
Subjective Logic

Default Reasoning for Forensic Visual Surveillance Based on Subjective Logic and its Comparison with L-Fuzzy Set Based Approaches

2013 ◽  
pp. 51-94
Author(s):  
Seunghan Han ◽  
Walter Stechele
Keyword(s):  
Fuzzy Set ◽  
Default Logic ◽  
Visual Surveillance ◽  
Default Reasoning ◽  
Illustrative Case ◽  
Reasoning Under Uncertainty ◽  
Subjective Logic ◽  
Continuous Space ◽  
Uncertainty Handling ◽  
Belief Theory

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.

Default Reasoning for Forensic Visual Surveillance based on Subjective Logic and Its Comparison with L-Fuzzy Set Based Approaches

2011 ◽  
Vol 2 (1) ◽  
pp. 38-86
Author(s):  
Seunghan Han ◽  
Walter Stechele
Keyword(s):  
Fuzzy Set ◽  
Default Logic ◽  
Visual Surveillance ◽  
Default Reasoning ◽  
Illustrative Case ◽  
Reasoning Under Uncertainty ◽  
Subjective Logic ◽  
Continuous Space ◽  
Uncertainty Handling ◽  
Belief Theory

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.

Great Expectations: Perceptual Challenges of Visual Surveillance in Lifeguarding

2012 ◽  
Author(s):  
Lyndsey K. Lanagan-Leitzel ◽  
Emily Skow ◽  
Cathleen M. Moore
Keyword(s):  
Visual Surveillance ◽  
Great Expectations

A Fast Method for Defogging of Outdoor Visual Images

2019 ◽  
Vol 12 ◽  
Author(s):  
Tannistha Pal
Keyword(s):  
Intelligent Vehicles ◽  
Visual Surveillance ◽  
Qualitative Assessment ◽  
Computational Time ◽  
Data Sets ◽  
Fast Method ◽  
Time Data ◽  
Image Defogging ◽  
Clear Vision ◽  
Dark Channel

Images captured in severe atmospheric catastrophe especially in fog critically degrade the quality of an image and thereby reduces the visibility of an image which in turn affects several computer vision applications like visual surveillance detection, intelligent vehicles, remote sensing, etc. Thus acquiring clear vision is the prime requirement of any image. In the last few years, many approaches have been made towards solving this problem. In this article, a comparative analysis has been made on different existing image defogging algorithms and then a technique has been proposed for image defogging based on dark channel prior strategy. Experimental results show that the proposed method shows efficient results by significantly improving the visual effects of images in foggy weather. Also computational time of the existing techniques are much higher which has been overcame in this paper by using the proposed method. Qualitative assessment evaluation is performed on both benchmark and real time data sets for determining theefficacy of the technique used. Finally, the whole work is concluded with its relative advantages and shortcomings.

scholarly journals More on Modal Aspects of Default Logic1

1992 ◽  
Vol 17 (1-2) ◽  
pp. 99-116
Author(s):  
V. Wiktor Marek ◽  
Miroslaw Truszczynski
Keyword(s):  
Modal Logic ◽  
Default Logic ◽  
Nonmonotonic Logic ◽  
Default Reasoning ◽  
Default Theory ◽  
Autoepistemic Logic ◽  
The Relationship ◽  
Natural Classes ◽  
Weak Extension

Investigations of default logic have been so far mostly concerned with the notion of an extension of a default theory. It turns out, however, that default logic is much richer. Namely, there are other natural classes of objects that might be associated with default reasoning. We study two such classes of objects with emphasis on their relations with modal nonmonotonic formalisms. First, we introduce the concept of a weak extension and study its properties. It has long been suspected that there are close connections between default and autoepistemic logics. The notion of weak extension allows us to precisely describe the relationship between these two formalisms. In particular, we show that default logic with weak extensions is essentially equivalent to autoepistemic logic, that is, nonmonotonic logic KD45. In the paper we also study the notion of a set of formulas closed under a default theory. These objects are shown to correspond to stable theories and to modal logic S5. In particular, we show that skeptical reasoning with sets closed under default theories is closely related with provability in S5. As an application of our results we determine the complexity of reasoning with weak extensions and sets closed under default theories.

scholarly journals Action Recognition Network Using Stacked Short-Term Deep Features and Bidirectional Moving Average

2021 ◽  
Vol 11 (12) ◽  
pp. 5563
Author(s):  
Jinsol Ha ◽  
Joongchol Shin ◽  
Hasil Park ◽  
Joonki Paik
Keyword(s):  
Action Recognition ◽  
High Speed ◽  
Moving Average ◽  
Video Clip ◽  
Visual Surveillance ◽  
Temporal Information ◽  
Feature Maps ◽  
Short Term ◽  
Accurate Analysis ◽  
Difference Images

Action recognition requires the accurate analysis of action elements in the form of a video clip and a properly ordered sequence of the elements. To solve the two sub-problems, it is necessary to learn both spatio-temporal information and the temporal relationship between different action elements. Existing convolutional neural network (CNN)-based action recognition methods have focused on learning only spatial or temporal information without considering the temporal relation between action elements. In this paper, we create short-term pixel-difference images from the input video, and take the difference images as an input to a bidirectional exponential moving average sub-network to analyze the action elements and their temporal relations. The proposed method consists of: (i) generation of RGB and differential images, (ii) extraction of deep feature maps using an image classification sub-network, (iii) weight assignment to extracted feature maps using a bidirectional, exponential, moving average sub-network, and (iv) late fusion with a three-dimensional convolutional (C3D) sub-network to improve the accuracy of action recognition. Experimental results show that the proposed method achieves a higher performance level than existing baseline methods. In addition, the proposed action recognition network takes only 0.075 seconds per action class, which guarantees various high-speed or real-time applications, such as abnormal action classification, human–computer interaction, and intelligent visual surveillance.

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