Object Detection using the Kinect

Abstract

We investigate an object detection system that uses both image and three-dimensional (3-D) point cloud data captured from the low-cost Microsoft Kinect vision sensor. The system works in three parts: image and point cloud data are fed into two components; the point cloud is segmented into hypothesized objects and the image region for those objects are extracted; and finally, a histogram of oriented gradient (HOG) descriptors are used for detection using a sliding window scheme. We evaluate this system by detecting backpacks on a challenging set of capture sequences in an indoor office environment with encouraging results.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2012
Accession Number
ADA564736

Entities

People

  • Jason Owens

Organizations

  • United States Army Research Laboratory

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Backpacks
  • Computer Vision
  • Data Sets
  • Detection
  • Detectors
  • Environment
  • Geometry
  • Histograms
  • Image Processing
  • Object Recognition
  • Operating Systems
  • Point Clouds
  • Recognition
  • Supervised Machine Learning
  • Three Dimensional
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Atmospheric Remote Sensing.
  • Database Systems and Applications
  • Robotics and Automation.