Continuous convolutional object tracking

Tracking arbitrary objects is a challenging task in visual computing. A central problem is the need to adapt to the changing appearance of an object, particularly under strong transformation and occlusion. We propose a tracking framework that utilises the strengths of Convolutional Neural Networks (CNNs) to create a robust and adaptive model of the object from training data produced during tracking. An incremental update mechanism provides increased performance and reduces training during tracking, allowing its real-time use.