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Content-Aware Focal Plane Selection and Proposals for Object Tracking on Plenoptic Image Sequences.

Dae Hyun Bae1, Jae Woo Kim2, Jae-Pil Heo3

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do 16419, Korea. noahark0628@gmail.com.

Sensors (Basel, Switzerland)
|December 26, 2018
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Summary
This summary is machine-generated.

This study introduces a new focus index selection algorithm for object tracking in computer vision, significantly improving performance during occlusions. The method enhances tracking accuracy and robustness using plenoptic image sequences.

Keywords:
bounding box proposalcontent-based image matchingobject trackingplenoptic imaging technique

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Area of Science:

  • Computer Vision
  • Image Processing

Background:

  • Object tracking is crucial for applications like surveillance and autonomous vehicles.
  • Occlusion presents a significant challenge for existing object tracking methods.
  • Plenoptic imaging offers potential solutions for occlusion handling due to its refocusing capabilities.

Purpose of the Study:

  • To propose a novel focus index selection algorithm for robust object tracking in plenoptic image sequences.
  • To improve tracking accuracy and reliability, especially when objects are occluded.

Main Methods:

  • Developed a focus index selection algorithm using focus measures and visual similarity.
  • Identified an optimal focal plane for tracking by finding the most focused and visually distinct plane.
  • Utilized the selected focus index to generate object proposals, estimate scale changes, and incorporate trajectory information.

Main Results:

  • The proposed method demonstrated higher accuracy and robustness compared to existing plenoptic tracking techniques.
  • Experimental evaluations on multiple plenoptic image sequences validated the effectiveness of the new algorithm.

Conclusions:

  • The novel focus index selection algorithm significantly enhances object tracking performance in challenging scenarios, particularly during occlusions.
  • The method leverages plenoptic imaging properties for more accurate scale estimation and trajectory prediction.