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Deep learning model based multimedia retrieval and its optimization in augmented reality applications.

Yash Prakash Gupta1, Mukul1, Nitin Gupta2

  • 1Department of Electronics Communication and Engineering, National Institute of Technology, Hamirpur, Himachal Pradesh India.

Multimedia Tools and Applications
|August 15, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing mobile augmented reality (AR) applications by offloading deep learning models to the cloud significantly impacts performance. This study proposes a framework to enhance AR performance through efficient cloud-based computation, reducing latency and improving graphics.

Keywords:
Augmented realityCloud computationDeep learningLatencyMTCNNMedia retrievalMedical augmented realityOffLoading

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Software Engineering

Background:

  • The proliferation of touchless technology and devices supporting Mixed Reality (MR), Augmented Reality (AR), and Virtual Reality (VR) has spurred innovation.
  • Mobile AR applications face performance challenges in graphics, latency, and CPU usage, particularly for real-time computations required by industries like medicine and education.
  • Offloading deep learning computations to cloud servers can negatively impact AR application performance parameters.

Purpose of the Study:

  • To propose and evaluate a framework for optimizing the deployment of mobile AR applications.
  • To enhance AR performance by optimizing multimedia retrieval, processing, and graphics augmentation through efficient cloud-client communication.
  • To address the performance bottlenecks in mobile AR applications requiring real-time computation.

Main Methods:

  • Utilized a Multi-Task Cascaded Convolutional Neural Network (MTCNN) for face detection within the AR application.
  • Developed a mobile AR application using Unity3D to virtually augment a 3D skeleton model onto a target face.
  • Focused on optimizing the communication and computation offloading between the local mobile application and cloud computing frameworks.

Main Results:

  • Achieved extremely low average Media Retrieval Time (1.1471 μs) and Client Time (1.1207 μs) within the local application.
  • Demonstrated significantly lower latency compared to the average API process time (288.934ms).
  • Identified that the highest time latency occurred at frame rates exceeding 80fps.

Conclusions:

  • The proposed framework effectively optimizes mobile AR application deployment by enhancing multimedia processing and graphics augmentation.
  • Efficient cloud-client communication is crucial for improving the performance parameters of mobile AR applications.
  • The study provides a viable solution for real-time computation requirements in AR, leading to enhanced user experiences.