20 citations · 20 across the 1 of their papers we have counts for
5 papers
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications. However, their accuracy is inadequate in ma…
Is First Person Vision Challenging for Object Tracking?
Matteo Dunnhofer, Antonino Furnari, Giovanni Maria Farinella +1
Understanding human-object interactions is fundamental in First Person Vision (FPV). Tracking algorithms which follow the objects manipulated by the camera wearer can provide usefu…
An Exploration of Target-Conditioned Segmentation Methods for Visual Object Trackers
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Visual object tracking is the problem of predicting a target object's state in a video. Generally, bounding-boxes have been used to represent states, and a surge of effort has been…
Tracking-by-Trackers with a Distilled and Reinforced Model
Matteo Dunnhofer, Niki Martinel, Christian Micheloni
Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we…
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator
Matteo Dunnhofer, Niki Martinel, Gian Luca Foresti +1
In the last decade many different algorithms have been proposed to track a generic object in videos. Their execution on recent large-scale video datasets can produce a great amount…