Alfredo Petrosino

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Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity(More)
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on shortterm tracking to date. For each participating tracker, a short description(More)
The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are 2 Authors Suppressed Due to Excessive Length presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For(More)
The Self-Organizing Background Subtraction (SOBS) algorithm implements an approach to moving object detection based on the neural background model automatically generated by a self-organizing method, without prior knowledge about the involved patterns. Such adaptive model can handle scenes containing moving backgrounds, gradual illumination variations and(More)
The detection of moving objects from stationary cameras is usually approached by background subtraction, i.e. by constructing and maintaining an up-to-date model of the background and detecting moving objects as those that deviate from such a model. We adopt a previously proposed approach to background subtraction based on self-organization through(More)
The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply prelearned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art(More)
Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization,(More)
We present a new short-term tracking algorithm called Best Displacement Flow (BDF). This approach is based on the idea of ‘Flock of Trackers’ with two main contributions. The first contribution is the adoption of an efficient clustering approach to identify what we term the ‘Best Displacement’ vector, used to update the object’s bounding box. This(More)