| Summary: | This research presents machine vision techniques to track an object of interest visually in an image sequence in which the target appearance, scale, orientation, shape, and position may significantly change over time. The images are captured using a non-stationary camera in a dynamic environment in a gray-scale format, and the initial location of the target is given.
The contributions of this thesis include the introduction of two robust object tracking techniques and an adaptive similarity measure which can significantly improve the performance of visual tracking.
In the first technique, the target is initially partitioned into several sub-regions, and subsequently each sub-region is represented by two distinct adaptive templates namely immediate and delayed templates. At every tracking step, the translational transformation of each sub-region is preliminarily estimated using the immediate template by a multi-start gradient-based search, and then the delayed template is employed to correct the estimation. After this two-step optimization, the target is tracked by robust fusion of the new sub-region locations. From the experiments, the proposed tracker is more robust against appearance variance and occlusion in comparison with the traditional trackers.
Similarly, in the second technique the target is represented by two heterogeneous Gaussian-based templates which models both short- and long-term changes in the target appearance. The target localization of the latter technique features an interactive multi-start optimization that takes into account generic transformations using a combination of sampling- and gradient-based algorithms in a probabilistic framework.
Unlike the two-step optimization of the first method, the templates are used to find the best location of the target, simultaneously.
This approach further increases both the efficiency and accuracy of the proposed tracker.
Lastly, an adaptive metric to estimate the similarity between the target model and new images is proposed. In this work, a weighted L2-norm is used to calculate the target similarity measure. A histogram-based classifier is learned on-line to categorize the L2-norm error into three classes which subsequently specify a weight to each L2-norm error. The inclusion of the proposed similarity measure can remarkably improve the robustness of visual tracking against severe and long-term occlusion.
|
|---|
