Tracking and fine-grained activity recognition in depth videos

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Tracking and activity recognition in video are arguably two of the most active topics within the field of computer vision and pattern recognition. Historically, tracking and activity recognition have been performed over conventional video such as color or grey-level frames, either of which contains significant clues for the identification of targets. While this is often a desirable feature within the context of video surveillance, the use of video for activity recognition or for tracking in privacy-sensitive environments such as hospitals and care facilities is often perceived as intrusive. For this reason, this PhD research has focused on providing tracking and activity recognition solely from depth videos which offer a naturally privacy-preserving visual representation of the scene at hand. Depth videos can nowadays be acquired with inexpensive and highly-available commercial sensors such as Microsoft Kinect and Asus Xtion. The two main contributions of this research have been the design of a specialised tracking algorithm for tracking in depth data, and a fine-grained activity recognition approach for recognising activities in depth video. The proposed tracker is an extension of the popular Struck algorithm, an approach that leverages a structural support vector machine (SVM) for tracking. The main contributions of the proposed tracker include a dedicated depth feature based on local depth patterns, a heuristic for handling view occlusions in depth frames, and a technique for keeping the number of support vectors within a given budget, so as to limit computational costs. Conversely, the proposed fine-grained activity recognition approach leverages multi-scale depth measurements and a Fisher-consistent multi-class SVM. In addition to the novel approaches for tracking and activity recognition, in this thesis we have canvassed and developed a practical computer vision application for the detection of hand hygiene at a hospital. This application was developed in collaboration with clinical researchers from the Intensive Care Unit of Sydney’s Royal Prince Alfred Hospital. Experiments presented through the thesis confirm that the proposed approaches are effective, and either outperform the state of the art or significantly reduce the need for sensor instrumentation. The outcomes of the hand-hygiene detection were also positively received and assessed by the clinical research unit.
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