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.

@InProceedings{SHW18, 
 	 author =  {Springstübe, Peer and Heinrich, Stefan and Wermter, Stefan},  
 	 title = {Continuous convolutional object tracking}, 
 	 booktitle = {Proceedings of the 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {73--78},
 	 year = {2018},
 	 month = {Apr},
 	 publisher = {},
 	 doi = {}, 
 }