Utility Person Detection and Multi-View Video Tracking Annotation Model
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Abstract
In this thesis a generic methodology for the semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video data sets. Most of the annotation data are automatically computed, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the data, composed of tracks with insufficient reliability, is verified by a human using a simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic and can handle additional trackers. In this thesis studied the most commonly used face edge detection techniques of Enhanced Sobel Edge Annotation Algorithm (ESEAA). Higher-level edge detection techniques and appropriate programming tools only facilitate the process but do not make it a simple task.
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