Context-Aware Mobile Capture and Sharing of Video Clips

2009 ◽  
pp. 1080-1095
Author(s):  
Janne Lahti ◽  
Utz Westermann ◽  
Marko Palola ◽  
Johannes Peltola
Keyword(s):  
Mobile Phones ◽  
Video Production ◽  
Context Aware ◽  
Video Content ◽  
Home Video ◽  
Video Clips ◽  
Camera Phones ◽  
Context Data ◽  
Effective Organization ◽  
Mobile Context

Video management research has been neglecting the increased attractiveness of using cameraequipped mobile phones for the production of short home video clips. But specific capabilities of modern phones — especially the availability of rich context data — open up new approaches to traditional video management problems, such as the notorious lack of annotated metadata for home video content. In this chapter, we present MobiCon, a mobile, context-aware home video production tool. MobiCon allows users to capture video clips with their camera phones, to semi-automatically create MPEG-7-conformant annotations by exploiting available context data at capture time, to upload both clips and annotations to the users’ video collections, and to share these clips with friends using OMA DRM. Thereby, MobiCon enables mobile users to effortlessly create richly annotated home video clips with their camera phones, paving the way to a more effective organization of their home video collections.

Context-Aware Mobile Capture and Sharing of Video Clips

2011 ◽  
pp. 340-356 ◽  
Author(s):  
Janne Lahti ◽  
Utz Westermann ◽  
Marko Palola ◽  
Johannes Peltola ◽  
Elena Vildjiounaite
Keyword(s):  
Mobile Phones ◽  
Video Production ◽  
Context Aware ◽  
Video Content ◽  
Home Video ◽  
Video Clips ◽  
Camera Phones ◽  
Context Data ◽  
Effective Organization ◽  
Mobile Context

Video management research has been neglecting the increased attractiveness of using camera-equipped mobile phones for the production of short home video clips. But specific capabilities of modern phones — especially the availability of rich context data — open up new approaches to traditional video management problems, such as the notorious lack of annotated metadata for home video content. In this chapter, we present MobiCon, a mobile, context-aware home video production tool. MobiCon allows users to capture video clips with their camera phones, to semi-automatically create MPEG-7-conformant annotations by exploiting available context data at capture time, to upload both clips and annotations to the users’ video collections, and to share these clips with friends using OMA DRM. Thereby, MobiCon enables mobile users to effortlessly create richly annotated home video clips with their camera phones, paving the way to a more effective organization of their home video collections.

Context-Aware Capture and Sharing of Mobile Videos

2011 ◽  
pp. 298-313
Author(s):  
Janne Lahti
Keyword(s):  
Mobile Phones ◽  
Video Production ◽  
Context Aware ◽  
Video Content ◽  
Home Video ◽  
Video Clips ◽  
Camera Phones ◽  
Context Data ◽  
Effective Organization ◽  
Mobile Context

Video management research has been neglecting the increased attractiveness of using camera-equipped mobile phones for the production of short personal video clips. But specific capabilities of modern phones—especially the availability of rich context data—open up new approaches to traditional video management problems, such as the notorious lack of annotated metadata for personal video content. In this chapter, we present MobiCon, a mobile, context-aware personal video production and sharing tool. MobiCon allows users to capture video clips with their camera phones, to semi-automatically create MPEG-7-conformant annotations by exploiting available context data at capture time, to upload both clips and annotations to the users’ video collections, and to share these clips with friends. Thereby, MobiCon enables mobile users to effortlessly create richly annotated home video clips with their camera phones, paving the way to a more effective organization of their personal video collections.

scholarly journals CONTEXT STORAGE USING CLOUD-BASED MONGODB WITH RULE-BASED RETE ALGORITHM

2021 ◽  
Vol 9 (2) ◽  
pp. 1022-1030
Author(s):  
Shivakumar. C, Et. al.
Keyword(s):  
Sensor Network ◽  
Context Awareness ◽  
Research Work ◽  
Context Aware ◽  
The Novel ◽  
Rule Based ◽  
Smart System ◽  
Context Aware Computing ◽  
Context Data ◽  
Day By Day

In this Context-aware computing era, everything is being automated and because of this, smart system’s count been incrementing day by day.  The smart system is all about context awareness, which is a synergy with the objects in the system. The result of the interaction between the users and the sensors is nothing but the repository of the vast amount of context data. Now the challenging task is to represent, store, and retrieve context data. So, in this research work, we have provided solutions to context storage. Since the data generated from the sensor network is dynamic, we have represented data using Context dimension tree, stored the data in cloud-based ‘MongoDB’, which is a NoSQL. It provides dynamic schema and reasoning data using If-Then rules with RETE algorithm. The Novel research work is the integration of NoSQL cloud-based MongoDB, rule-based RETE algorithm and CLIPS tool architecture. This integration helps us to represent, store, retrieve and derive inferences from the context data efficiently..                       

scholarly journals Preserving User Privacy and Security in Context-Aware Mobile Platforms

2019 ◽  
pp. 1203-1230 ◽  
Author(s):  
Prajit Kumar Das ◽  
Dibyajyoti Ghosh ◽  
Pramod Jagtap ◽  
Anupam Joshi ◽  
Tim Finin
Keyword(s):  
Context Aware ◽  
Context Modeling ◽  
User Privacy ◽  
Mobile Platforms ◽  
Privacy And Security ◽  
Semantic Web Technologies ◽  
Fine Grained ◽  
New Class ◽  
Context Data ◽  
Collaborative Context

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

scholarly journals Mobile Visual Communication

2007 ◽  
Vol 28 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Mikko Villi
Keyword(s):  
Mobile Phones ◽  
Visual Communication ◽  
Cellular Phone ◽  
Camera Phone ◽  
Visual Contact ◽  
Mobile Cellular ◽  
Camera Phones ◽  
Potential Part ◽  
Personal Photography

Abstract In this article I will elucidate the concept of photo messaging, and examine camera phones in the context of communication and photography. Camera functions are nowadays a popular add-on to the mobile (cellular) phone. Users can send photographs directly from the phone as photo messages. Findings suggest that the ubiquitous camera phone, and photo messaging, may substantially change the ways in which people use personal photography. The imaging capacity of mobile phones is becoming a potential part of perpetual visual contact. Thus taking and sending photographs on a camera phone represents a new resource for visual communication.

Reinforcement and Non-Reinforcement Machine Learning Classifiers for User Movement Prediction

2013 ◽  
pp. 218-237 ◽  
Author(s):  
Theodoros Anagnostopoulos
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Location Prediction ◽  
Context Aware ◽  
Resonance Theory ◽  
Self Organizing Maps ◽  
Adaptive Resonance ◽  
Proposed Model ◽  
On Line ◽  
Mobile Context

Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify, and predict context in order to act efficiently, beforehand, for the benefit of the user. In this chapter, the authors propose a mobility prediction model, which deals with context representation and location prediction of moving users. Machine Learning (ML) techniques are used for trajectory classification. Spatial and temporal on-line clustering is adopted. They rely on Adaptive Resonance Theory (ART) for location prediction. Location prediction is treated as a context classification problem. The authors introduce a novel classifier that applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. They compare ART with Self-Organizing Maps (SOM), Offline kMeans, and Online kMeans algorithms. Their findings are very promising for the use of the proposed model in mobile context aware applications.

Context-Aware Presentation of Linked Data on Mobile

2018 ◽  
pp. 1940-1971
Author(s):  
Luca Costabello ◽  
Fabien Gandon
Keyword(s):  
Linked Data ◽  
Edit Distance ◽  
Research Question ◽  
Context Aware ◽  
Graph Edit Distance ◽  
Optimal Error ◽  
Presentation Level ◽  
Rdf Graphs ◽  
Mobile Context ◽  
Proper Context

In this paper the authors focus on context-aware adaptation for linked data on mobile. They split up the problem in two sub-questions: how to declaratively describe context at RDF presentation level, and how to overcome context imprecisions and incompleteness when selecting the proper context description at runtime. The authors answer their two-fold research question with PRISSMA, a context-aware presentation layer for Linked Data. PRISSMA extends the Fresnel vocabulary with the notion of mobile context. Besides, it includes an algorithm that determines whether the sensed context is compatible with some context declarations. The algorithm finds optimal error-tolerant subgraph isomorphisms between RDF graphs using the notion of graph edit distance and is sublinear in the number of context declarations in the system.

Sign in / Sign up

Export Citation Format

Share Document