Browsing by Author "Cameron, James A. D."

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    Automation of the Timed-Up-and-Go test using a conventional video camera
    (IEEE, 2019-08-09) Savoie, Patrick; Cameron, James A. D.; Kaye, Mary E.; Scheme, Erik J.
    The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more resolved information about its sub-phases. While some approaches have shown promise, they often require the donning of sensors or the use of specialized hardware, such as the now discontinued Microsoft Kinect, which combines video information with depth sensors (RGBD). In this work, we leverage recent advances in computer vision to automate the TUG test using a regular RGB video camera without the need for custom hardware or additional depth sensors. Thirty healthy participants were recorded using a Kinect V2 and a standard video feed while performing multiple trials of 3 and 1.5 meter versions of the TUG test. A Mask Regional Convolutional Neural Net (R-CNN) algorithm and a Deep Multitask Architecture for Human Sensing (DMHS) were then used together to extract global 3D poses of the participants. The timing of transitions between the six key movement phases of the TUG test were then extracted using heuristic features extracted from the time series of these 3D poses. The proposed video-based vTUG system yielded the same error as the standard Kinect-based system for all six key transitions points, and average errors of less than 0.15 seconds from a multi-observer hand labeled ground truth. This work describes a novel method of video-based automation of the TUG test using a single standard camera, removing the need for specialized equipment and facilitating the extraction of additional meaningful information for clinical use.
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    Dynamic visual data prioritization in automated object detection systems for multi-camera surveillance
    (University of New Brunswick, 2019) Cameron, James A. D.; Kaye, Mary; Scheme, Erik
    Modern automated object detection systems are key tools in surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data created by surveillance cameras. Due to the nature of surveillance systems, this visual data must be processed accurately and in real-time. However, many of the frames that are created by the surveillance cameras may be of low importance, providing no useful information to the object detection system. Sub-sampling the surveillance data by prioritizing important camera frames can greatly reduce unnecessary computation. Several works have been conducted on dynamic visual data sub-sampling using various modalities of information (ie. spatial or temporal information) for prioritization. However, few works have used different modalities of information together for visual data prioritization. Furthermore, given the fast pace of the research space, only a small subset of works have implemented visual data prioritization with modern computer vision algorithms in mind. This work evaluates several individual prioritization metrics, that use different modalities of information to prioritize visual data, for use with modern object detection algorithms. This thesis presents an ensemble method that uses a KNN regressor to combine the best of the previously evaluated metrics. This dynamic approach was shown to increase the detection rate in an indoor surveillance scenario by over 60% compared to a static sub-sampling baseline.