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Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.

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Study on the Moving Target Tracking Based on Vision DSP.

Xuan Gong1, Zichun Le2, Hui Wang1

  • 1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China.

Sensors (Basel, Switzerland)
|November 18, 2020
PubMed
Summary
This summary is machine-generated.

Hardware optimization methods enhance embedded visual tracking systems on limited resources. Implementing a kernel correlation filter (KCF) on a vision digital signal processor (DSP) improved real-time performance and accuracy.

Keywords:
DSSTKCFSIMDdata parallelismiDMAinstruction parallelismruntimevision DSP

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Area of Science:

  • Computer Vision
  • Embedded Systems
  • Digital Signal Processing

Background:

  • Embedded visual tracking demands high real-time performance and system resources, posing challenges for systems with limited hardware.
  • Optimizing hardware resource utilization is crucial for efficient visual tracking algorithms.

Purpose of the Study:

  • To evaluate hardware optimization methods for embedded visual tracking systems.
  • To investigate the real-time performance of a kernel correlation filter (KCF) algorithm on a vision digital signal processor (DSP).

Main Methods:

  • Implemented and optimized a KCF tracking algorithm on a vision DSP.
  • Analyzed the impact of data parallelism (DP), instruction parallelism (IP), DSP core parallel processing, and integrated direct memory access (iDMA).
  • Introduced a scale filter to address KCF's limitations with scale transformation and employed a time-sharing strategy to boost system speed.

Main Results:

  • The optimized KCF algorithm demonstrated efficient utilization of limited hardware resources.
  • Real-time tracking speed and system resource usage met expected requirements.
  • The KCF algorithm with a scale filter achieved accuracy comparable to the discriminative scale space tracking (DSST) algorithm.

Conclusions:

  • Hardware optimization techniques effectively improve the performance of embedded visual tracking systems.
  • The KCF algorithm, enhanced with a scale filter and optimized for vision DSP, offers a viable solution for real-time visual tracking with limited resources.
  • The findings are applicable to other machine vision algorithms requiring efficient hardware utilization.