Independent selection and validation for tracking-learning-detection
Helena de Almeida Maia, Fábio Luiz Marinho de Oliveira, Marcelo Bernardes Vieira · 2016
On the problem of tracking objects in videos, a recent and distinguished approach combining tracking and detection methods is the TLD framework. The detector identifies the object by its supposedly confirmed appearances. The tracker inserts new appearances into the model using apparent motion. Their outcomes are integrated by using the same similarity metric of the detector which, in our point of view, leads to biased results. We propose a mediator method to integrate the motion tracker and detector by combining their estimations. Our results show that when the mediaton strategy is independent of both tracker/detector metrics, the overall tracking is improved for objects with high appearance variations throughout the video.