An equalised global graphical model-based approach for multi-camera object tracking
Abstract— Multi-camera non-overlapping visual object tracking system typically consists of two tasks: single camera object tracking and inter-camera object tracking. Since the state-of-theart approaches are yet not perform perfectly in real scenes, the errors in single camera object tracking module would propagate into the module of inter-camera object tracking, resulting much lower overall performance. In order to address this problem, we develop an approach that jointly optimise improve the single camera object tracking and inter-camera object tracking in an equalised global graphical model. Such an approach has the advantage of guaranteeing a good < Final Year Projects 2016 >overall tracking performance even when there are limited amount of false tracking in single camera object tracking. Besides, the similarity metrics used in our approach improve the compatibility of the metrics used in the two different tasks. Results show that our approach achieve the state-of-the-art results in multi-camera non-overlapping tracking datasets.
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