Alfredo Petrosino

TL;DR: This work proposes an approach based on self organization through artificial neural networks, widely applied in human image processing systems and more generally in cognitive science, that can handle scenes containing moving backgrounds, gradual illumination variations and camouflage, and achieves robust detection for different types of videos taken with stationary cameras.

TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.

TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.

TL;DR: The evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset are presented, offering a more systematic comparison of the trackers.