| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 99 === Recently, tracking using adaptive appearance models is popular. Tracking algorithms adopting an adaptive appearance model are simple and fast, but suffer from drifting problems caused by background interference. The drifting problem, resulting in inaccuracy, comes from the accumulation of slight labeling errors occur in updating model in each tracking iteration. Taking online boosting for tracking (OBT) as the basis, we introduce depth, multiple scales and lifetimer to our algorithm (named Enhanced OBT; also abbreviate to EOBT) and eliminate drifting problems induced by background interference. In EOBT, depth can be used to filter out the background data, the racker with multiple scales can be used to improve the accuracy, and
dynamically adjusted lifetimer can be used to determine whether the object is temporarily occluded. Since conventional evaluation method of accuracy may derive a high accuracy when an algorithm tracks a wrong target, we additionally design two ratios (`Ratio in Object' and `Ratio in Tracker') to avoid such a problem and precisely evaluate the accuracy. In our method, `Ratio in Object' shows the percentage of an object caught by a tracker, while the `Ratio in Tracker' reflects the percentage of a tracker occupied by the object to be tracked. In this thesis, we conduct several experiments to show that EOBT can effectively reduce drifting problems and improve the accuracy of object tracking.
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