Distinguished Student Work



Download Primary File (718 KB)


The purpose of this design experiment was to attempt to determine the accuracy/possibility of using Kalman filters to approximate a human’s location when blocked by an occlusion/obstacle. This could potentially be implemented in the field of human-robot interaction to allow humans and robots to collaborate without interference. The outcome of this research project could similarly result in more accurate measurements for human body detection.

This project consists of the following two different procedures: 1) design steps of the solution, 2) testing final design iteration. In order to construct the hypothetical solution to the project, the first procedure must be followed, while to test the solution, the second procedure must be followed. Each procedure is extremely descriptive, explaining the several steps to building and testing the experimental solution precisely.

Based on the results of the experiment, the problem statement was proved inconclusive due to both positive and negative results. Throughout all the data collected and graphs constructed, the Kalman filter’s average mean-squared errors were never concretely/always lower than those of the ZED depth camera. The project’s findings reveal the inconclusive and ambiguous behavior of the Kalman filter, indicating more trials and testing is needed for a concrete determination.

Publication Date




  • Illinois Junior Academy of Science - State Science Fair Qualifier
  • Illinois State Academy of Science - Invited to present as one of the top two projects from this region


  • Rui Chen; Carnegie Mellon University
  • Changliu Liu, PhD; Carnegie Mellon University

Human Body Detection with Occlusion



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.