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Multi-object model-free tracking is challenging because the tracker is not aware of the objects' type (not allowed to use object detectors), and needs to distinguish one object from background as well as other similar objects. Most existing methods keep updating their appearance model individually for each target, and their performance is hampered by sudden appearance change and/or occlusion. We propose...
The goal of model-based object trackers is to automatically detect and track specific objects, such as cars or pedestrians. To solve this problem, many modern trackers train a detector on a collection of annotated object images and use the trained detector in a tracking-by-detection framework. A major limitation of such an approach is that a single, generic detector is used to track specific objects,...
One fundamental problem of object tracking is the convergence of estimates to local maxima not corresponding to target objects. To mitigate this problem, constructing a good posterior distribution of the target state is important. In this letter, we propose a robust tracking approach by building a new posterior distribution model from multiple independent estimates of a target state. For each candidate...
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