Visual Object Tracking is one of the principal challenges in Computer Vision, where the task is to locate a certain object in all frames of a video, given only its location in the first frame.

Computer vision

Some example frames showing the scale adaptation of our approach.

Due to variations of appearance, the model generated from the first frame needs to be updated on the fly. We address this problem by online machine learning approaches, more concretely, discriminative correlation filters.

This video illustrates online adaptation of feature (color) selection to the model appearance.

Application scenario within WASP where the developed code has been used for people tracking:




External partners

International VOT benchmark committee


Cover of publication ''
Martin Danelljan, Goutam Bhat, Susanna Gladh, Fahad Shahbaz Khan, Michael Felsberg (2019)

Pattern Recognition Letters , Vol.124 , s.74-81 Continue to DOI

Cover of publication ''
Martin Danelljan, Gustav Häger, Fahad Shahbaz Khan, Michael Felsberg (2017)

IEEE Transactions on Pattern Analysis and Machine Intelligence , Vol.39 , s.1561-1575 Continue to DOI

Unveiling the Power of Deep Tracking, Goutam Bhat , Joakim Johnander, Martin Danelljan,  Fahad Shahbaz Khan, and Michael Felsberg, Computer Vision Foundation 2018.