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An FPGA Accelerator for High-Speed Moving Objects Detection and Tracking With a Spike Camera.

Yaoyu Zhu1, Yu Zhang2,3, Xiaodong Xie4

  • 1School of Electronic Engineering and Computer Science, Peking University, Beijing 100871, China yaoyu.zhu@pku.edu.cn.

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|July 7, 2022
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We developed a novel neural-inspired system for ultra-high-speed object detection and tracking using a spike camera. This system significantly accelerates processing, enabling real-time analysis for critical applications.

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Area of Science:

  • Computer Vision
  • Neuromorphic Engineering
  • High-Speed Imaging

Background:

  • Ultra-high-speed object detection and tracking are vital for applications like fault detection and scientific observation.
  • Current methods struggle with the demanding processing speeds required for these tasks.

Purpose of the Study:

  • To propose a neural-inspired scheme for ultra-high-speed moving object filtering, detection, and tracking.
  • To design and implement a hardware accelerator for this scheme using a high-speed spike camera.

Main Methods:

  • Parallelizing filtering and detection modules using block-based computation and a parallel connected component labeling algorithm.
  • Optimizing hardware by using multiplexers for LIF layers and fixed-point multiplications for exponential operations.
  • Implementing the accelerator on a Xilinx ZCU-102 board for validation.

Main Results:

  • Achieved a 19x acceleration compared to the serial version through 25-way parallelization.
  • The accelerator processes over 20,000 spike images (250x400 resolution) per second.
  • Demonstrated low dynamic power consumption of 1.618 W.

Conclusions:

  • The proposed neural-inspired scheme and accelerator effectively address the speed limitations of existing object detection and tracking systems.
  • The system offers a viable solution for real-time, high-speed visual analysis in demanding scientific and industrial fields.
  • Hardware optimizations significantly enhance processing speed and reduce resource consumption.