Nicoletta Noceti

• Multi-class image classification: sparsity does it better, Sean Ryan Fanello, Nicoletta Noceti, Giorgio Metta, Francesca Odone, VISAPP: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications., 2013
• Combining retrieval and classification for real-time face recognition, Giovanni Fusco, Nicoletta Noceti, Francesca Odone, Proc. of AVSS 2012, 2012
• Learning common behaviors from large sets of unlabeled temporal series, Nicoletta Noceti, Francesca Odone, Image and Vision Computing, 2012
• A Vision-Based Navigation Facility for planetary Entry Descent Landing, Piergiorgio Lanza, Nicoletta Noceti, Corrado Maddaleno, Antonio Toma, Luca Zini, Francesca Odone, Proc. of ECCV 2012, 2012
• Learning Behavioral Patterns of Time Series for Video-Surveillance, Francesca Odone, Nicoletta Noceti, Matteo Santoro, Machine Learning for Vision-Based Motion Analysis, 2011

• Action understanding
• 3D object recognition for robotics
• 3D modeling and location
• Behavior analysis
• Object detection

My research focuses on statistical learning and its application to behavior analysis problems in the field of video-surveillance tasks and automatic monitoring.

Traditional approaches based on motion analysis address many interesting applications, such as access control, anomaly detection, congestion analysis and multi-camera event description: in all these cases it is common practice to devise a measurement phase that extracts low level information from videos.

A well known limit of these methods is that while they provide effective tools to model the dynamics of a single video, they do not suffice when the problem of interest requires a higher generalization level. In the case of behaviors modeling or dynamic events classification, in particular, it is advisable to increase the abstraction of the data, designing higher-level descriptions able to model more general structures.

We may exploit the availability of possibly huge sets of examples, acquired by long time observations, and endowed with an internal structure provided by temporal coherence.

Within this framework the main goals of my research are:

• studying and developing robust methods to retrieve space-time information from videos
• studying and developing higher-level descriptions, to include space-time information within a machine learning framework
• devising machine learning strategies to model common events and anomalies from huge sets of (possibly) unlabeled examples.