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A Real-Time High Performance Computation Architecture for Multiple Moving Target Tracking Based on Wide-Area Motion
Kui Liu1, Sixiao Wei2, Zhijiang Chen3
1Intelligent Fusion Technology, Germantown, MD 20876, USA. kui.liu@intfusiontech.com.
Sensors (Basel, Switzerland)
|February 18, 2017
Summary
This study introduces a novel system combining Cloud and Graphics Processing Units (GPUs) for real-time Wide Area Motion Imagery (WAMI) analysis. This approach significantly enhances multiple moving target detection and tracking, outperforming traditional CPU-based methods.
Area of Science:
- Computer Vision
- High-Performance Computing
- Cloud Computing
Background:
- Wide Area Motion Imagery (WAMI) presents challenges for real-time multiple moving target detection and tracking.
- Traditional methods relying solely on Central Processing Units (CPUs) often struggle with the computational demands of WAMI data.
- Integrating advanced computing architectures is crucial for improving the efficiency and accuracy of WAMI analysis.
Purpose of the Study:
- To present the first complementary integration of Cloud computing and Graphics Processing Units (GPUs) for real-time high-performance computation.
- To develop and evaluate a GPU and Cloud Moving Target Tracking (GC-MTT) system for WAMI applications.
- To demonstrate the system's applicability to various tracking scenarios, including pedestrian tracking, group tracking, and Patterns of Life (PoL) analysis.
Main Methods:
- Developed a GPU and Cloud Moving Target Tracking (GC-MTT) system utilizing a front-end web server.
- Implemented highly parallelized computation functions using Compute Unified Device Architecture (CUDA©).
- Leveraged Hadoop for cloud-based data interaction and processing.
Main Results:
- The GC-MTT system achieves efficient real-time target recognition and tracking.
- Demonstrated drastically improved tracking performance with low frame rates under realistic conditions compared to CPU-only methods.
- Validated the effectiveness of combining Cloud and GPU resources for WAMI analysis.
Conclusions:
- The synergistic use of Cloud and GPUs offers a significant advancement in real-time WAMI target detection and tracking.
- The GC-MTT system provides a robust and efficient solution for complex tracking tasks.
- The methodology is extensible to other critical applications like pedestrian and group tracking, and Patterns of Life analysis.
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