A Mobile Augmented Reality System with Distributed Tracking

Evers-Senne, Jan-Friso; Schiller, Ingo; Petersen, Arne; Koch, Reinhard

doi:10.1109/3dpvt.2006.14

Cited by 4 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the sender and receiver in [4] could easily be heterogeneous devices. For example, in some proposed Augmented Reality (AR) scenarios [5], [3], the sender is mobile device with limited computing capabilities and the receiver is a more powerful server. Thus, in this paper, we propose an alternative FD architecture referred to as FD2 which shifts the controller's optimization problem to the receiver side.…”

Section: B Refined Methodologymentioning

confidence: 99%

“…To reduce the energy consumption, temperature, etc. on an individual mobile platform, some works have investigated ways of sharing tasks among other mobile devices [4] and high powered server computers [5], [3]. However, in order to share tracking workload across system boundaries, video data must be shared as well which requires heavy bandwidth utilization and communication energy.…”

mentioning

confidence: 99%

“…Mobile devices are advantageous because they can be easily deployed on stationary or moving objects in the environment under observation. Another interesting application for smart phones is Augmented Reality (AR) [5], [3]. In mobile AR, a camera-enabled device captures a real world scene and virtual objects/information are overlaid on that scene to enhance user perception, provide navigation, educate, etc.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Adaptable architectures for distributed visual target tracking

Forte

Srivastava

2011

2011 IEEE 29th International Conference on Computer Design (ICCD)

View full text Add to dashboard Cite

There are a growing number of visual tracking applications for mobile devices. However, the computer vision algorithms which process real-time video to track moving targets are demanding. Since a single mobile device possesses limited computational capabilities, energy, etc. to fully support target tracking, some works have investigated architectures which migrate a portion of tracking duties to another device at the cost of transmission bandwidth and energy. In this paper, we investigate the resource utilization in such architectures and present an adaptable architecture which balances tracking workload among the participating devices based on current resource availability (energy, temperature, bandwidth). Results show that the proposed solution requires low additional overhead, can improve on tracking system lifetime by reducing energy consumption, and is more effective in maintaining safe operating temperatures within participants as compared to previously investigated architectures. I. INTRODUCTIONThe widespread availability, portability, and built-in cameras make smart phones and other mobile devices a cheap and interesting choice for many embedded computer vision applications. For example, several papers have investigated the use of mobile phones in surveillance networks [1], [13]. Mobile devices are advantageous because they can be easily deployed on stationary or moving objects in the environment under observation. Another interesting application for smart phones is Augmented Reality (AR) [5], [3]. In mobile AR, a camera-enabled device captures a real world scene and virtual objects/information are overlaid on that scene to enhance user perception, provide navigation, educate, etc.The above applications require identifying and tracking people/objects in real-time video, but realizing them on individual mobile devices is challenging for several reasons: 1) Typical CPU hardware on mobile devices may not be able to handle the computational/memory demands of tracking algorithms.2) The lifetime of battery-powered mobile devices is limited by their energy consumption, which is proportional to their computational workload. 3) Excessive power dissipation can lead to high temperatures and overheating is known to cause reliability issues in electronic hardware. To reduce the energy consumption, temperature, etc. on an individual mobile platform, some works have investigated ways of sharing tasks among other mobile devices [4] and high powered server computers [5], [3]. However, in order to share tracking workload across system boundaries, video data must be shared as well which requires heavy bandwidth utilization and communication energy.In this paper, we investigate the resource requirements (energy, temperature, bandwidth) of distributed algorithms and architectures for a generic target tracking scenario. Specifically, we examine a two node system with one camera node

show abstract

Section: B Refined Methodologymentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Adaptable architectures for distributed visual target tracking

Forte

Srivastava

2011

2011 IEEE 29th International Conference on Computer Design (ICCD)

View full text Add to dashboard Cite

show abstract

“…Mobile devices are advantageous because they can easily be deployed on stationary or moving objects in the environment under observation. Another interesting application for smart phones is Augmented Reality (AR) [Evers-Senne et al 2007;Gammeter et al 2010]. In mobile AR, a camera-enabled device captures a real world scene and virtual objects/information are overlaid on that scene to enhance user perception, provide navigation, educate, etc.…”

Section: Introductionmentioning

confidence: 99%

Resource-aware architectures for adaptive particle filter based visual target tracking

Forte

Srivastava

2013

ACM Trans. Des. Autom. Electron. Syst.

View full text Add to dashboard Cite

There are a growing number of visual tracking applications now being envisioned for mobile devices. However, since computer vision algorithms such as particle filtering have large computational demands, they can result in high energy consumption and temperatures in mobile devices. Conventional approaches for distributed target tracking with a camera node and a receiver node are either sender-based (SB) or receiver-based (RB). The SB approach uses little energy and bandwidth, but requires a sender with large computational resources. The RB approach fits applications where computational resources are completely unavailable to the sender, but requires very large energy and bandwidth. In this article, we propose three architectures for distributed particle filtering that (i) reduce particle filtering workload and (ii) allow for dynamic migration of workload between nodes participating in tracking. We also discuss an adaptive particle filtering extension that adapts particle filter computational complexity and can be applied to both the conventional and proposed architectures for improved energy efficiency. Results show that the proposed solutions require low additional overhead, improve on tracking system lifetime, balance node temperatures, maintain track of the desired target, and are more effective than conventional approaches in many scenarios. ACM Reference Format:Forte, D. and Srivastava, A. 2013. Resource-aware architectures for adaptive particle filter based visual target tracking.

show abstract

“…A framework for distributed AR is described in [16], which focuses on relieving the computational burden on lightweight mobile devices by transferring recognition and pose computations to a central server. Our system makes use of TriCodes, artificial landmarks that can be detected and identified rapidly even on a handheld device with minimal processing power.…”

Section: Related Workmentioning

confidence: 99%

An Augmented Reality Interface for Mobile Information Retrieval

Mooser

Wang

You

et al. 2007

Multimedia and Expo, 2007 IEEE International Conference On

View full text Add to dashboard Cite

Recent years have seen growing interest in mobile augmented reality. The ability to retrieve information and display it as virtual content overlaid on top of an image of the real world is a natural extension to a mobile device equipped with a camera and wireless connectivity. Such applications need to address a number of technical hurdles including target recognition and camera pose estimation. Beyond these fundamental challenges, however, there is the problem of data connectivity and user interface presentation. How do we connect mobile clients to multiple data sources that may be changing in real time and provide the user with a flexible interface for navigating through the relevant content? We present an AR user interface framework specifically designed to expose disparate data sources through a single application server. Our proposed system uses an multi-tier architecture to separate back-end data retrieval from front-end graphical presentation and UI event handling. We describe how it might be used to build an oil platform equipment maintenance system, as an example of a collaborative, data-driven mobile application.

show abstract

A Mobile Augmented Reality System with Distributed Tracking

Cited by 4 publications

References 11 publications

Adaptable architectures for distributed visual target tracking

Adaptable architectures for distributed visual target tracking

Resource-aware architectures for adaptive particle filter based visual target tracking

An Augmented Reality Interface for Mobile Information Retrieval

Contact Info

Product

Resources

About