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Enhancing Embedded Object Tracking: A Hardware Acceleration Approach for Real-Time Predictability.

Mingyang Zhang1, Kristof Van Beeck1, Toon Goedemé1

  • 1PSI-EAVISE Research Group, Department of Electrical Engineering, KU Leuven, 2860 Sint-Katelijne-Waver, Belgium.

Journal of Imaging
|March 27, 2024
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Summary
This summary is machine-generated.

This study enhances real-time object tracking on embedded systems using Field-Programmable Gate Arrays (FPGAs). Hardware acceleration significantly reduces execution time and latency variation for predictable performance.

Keywords:
FPGAdeep learningembedded systemhardware accelerationhigh-level synthesisobject trackingreal-time system predictabilitysiamese network

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Area of Science:

  • Computer Vision
  • Embedded Systems Engineering
  • Hardware Acceleration

Background:

  • Siamese object tracking advancements have not adequately addressed hard real-time predictability on embedded devices.
  • Minimal and predictable execution latency is critical for many embedded applications.
  • Deep learning-based video object tracking systems face challenges in real-time performance.

Purpose of the Study:

  • To analyze and improve real-time predictability in deep learning-based video object tracking on embedded systems.
  • To identify and address time predictability bottlenecks within the tracking system.
  • To evaluate the effectiveness of hardware acceleration for real-time object tracking.

Main Methods:

  • Detailed analysis of real-time predictability across system components.
  • Implementation of dedicated hardware accelerators for depth-wise cross-correlation and padding using High-Level Synthesis (HLS).
  • Deployment and testing on a KV260 embedded board.

Main Results:

  • Field-Programmable Gate Array (FPGA) implementations demonstrate superior hard real-time behavior.
  • Hardware acceleration resulted in a 6.6x speedup in mean execution time.
  • Latency variation was reduced by 11 times compared to the baseline, significantly improving predictability.
  • Enhanced power efficiency was observed.

Conclusions:

  • Hardware acceleration is crucial for achieving time-predictable object tracking on embedded systems.
  • Dedicated hardware accelerators for key operations effectively address performance bottlenecks.
  • The developed approach sets new standards for hardware-software co-design in real-time embedded vision.