Computer vision study demonstrates real-time simultaneous tracking of multiple 3D objects in video sequences, highlighting enhanced scalability and stability for augmented reality.
We present a method that is able to track several 3D objects simultaneously, robustly, and accurately in real-time. While many applications need to consider more than one object in practice, the existing methods for single object tracking do not scale well with the number of objects, and a proper way to deal with several objects is required. Our method combines object detection and tracking: Frame-to-frame tracking is less computationally demanding but is prone to fail, while detection is more robust but slower. We show how to combine them to take the advantages of the two approaches, and demonstrate our method on several real sequences.
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Park et al. (2008) studied this question.
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