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Nowadays, we have several powerful architectures, e.g. YOLO, that allows us to find bounding boxes on the fly. Single-object tracking focus on the processing of sequences of RGB images to be able to identify and track a given object, which can be costly in terms of memory/computation. The main idea being this project is to use the bounding boxes itself and try to predict its movement based on the n-previous frames. By using this higher-level abstraction of the scene itself we might reduce the complexity and training time required for traditional Single-Object tracking. To start, we can use Kitti dataset to create such a prediction system and exploit other possible datasets/possible settings as soon as we have a working prototype.
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