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Vision-Based Fall Detection Using Confidence Prediction and Motion Analysis

Ros, Dara
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1649763590129093

Abstract Details

2022, MS, University of Cincinnati, Engineering and Applied Science: Electrical Engineering.
There are many research interests in human activity recognition, especially fall which is the major cause of serious injury for the elderly. Different technologies have been developed to detect falls, and fortunately, advancement in computer vision has attracted researchers to apply sophisticated systems for action recognition, posture estimation, and fall detection. The vision-based approach provides a non-invasive and reliable solution for fall detection among these various technologies. The overall goal of this thesis is to propose automatic human fall detection frameworks using confidence prediction and motion analysis. This thesis is composed of two pieces of work. The first work introduces a confidence-based fall detection system using multiple surveillance cameras. First, a model for predicting the confidence of fall detection on a single camera is constructed using a set of simple yet useful features. Then, the detection results from multiple cameras are fused based on their confidence levels. The proposed confidence prediction model can be easily implemented and integrated with single-camera fall detectors, and the proposed system improves the accuracy of fall detection through effective data fusion. Secondly, a flexible fall detection framework based on detecting a human object and analyzing the object’s motion is proposed. Unlike many state of the art that require predefined thresholds to detect a fall, the proposed framework localizes and tracks a person in videos via object detection and motion analysis over a time window with appropriate length. As fall events may not look the same from different view angles, a multi-view fall dataset is used to train the proposed detection method. The framework is flexible for different use cases as it could incorporate unique object detection methods and work on videos captured from different angles. The proposed framework has produced promising detection results on several other datasets that outperform two traditional methods.
Rui Dai, Ph.D. (Committee Member)
Xuefu Zhou, Ph.D. (Committee Member)
Wen-Ben Jone, Ph.D. (Committee Member)
42 p.
Electrical Engineering
Fall detection; motion analysis; vision-based

Recommended Citations

Citations

  • Ros, D. (2022). Vision-Based Fall Detection Using Confidence Prediction and Motion Analysis [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1649763590129093

    APA Style (7th edition)

  • Ros, Dara. Vision-Based Fall Detection Using Confidence Prediction and Motion Analysis. 2022. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1649763590129093.

    MLA Style (8th edition)

  • Ros, Dara. "Vision-Based Fall Detection Using Confidence Prediction and Motion Analysis." Master's thesis, University of Cincinnati, 2022. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1649763590129093

    Chicago Manual of Style (17th edition)

ucin1649763590129093
283
© 2022, some rights reserved.Creative Commons License Vision-Based Fall Detection Using Confidence Prediction and Motion Analysis by Dara Ros is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.