Publications by Rita Cucchiara

Achieving both localization and identication of people ina wide open area using only cameras can be a challengingtask, which requires cross-cutting requirements : high reso-lution for identication, whereas low resolution for having awide coverage of the localization. Consequently, this paperproposes the joint use of cameras (only devoted to local-ization) and RFID sensors (devoted to identication) withthe nal objective of detecting and localizing intruders. Toground the observations on a common coordinate system,a calibration procedure is dened. This procedure only de-mands a training phase with a single person moving in thescene holding a RFID tag. Although preliminary, the resultsdemonstrate that this calibration is sufficiently accurate tobe applied whenever dierent scenarios, where area of over-lap between the eld of view (FoV) of a camera and theField of sense" (FoS) of a (blind) sensor must be efficientlydetermined.

In this paper we define a new paradigm for 8-connection labeling, which employes a general approach to improve neighborhood exploration and minimizes the number of memory accesses. Firstly we exploit and extend the decision table formalism introducing OR-decision tables, in which multiple alternative actions are managed. An automatic procedure to synthesize the optimal decision tree from the decision table is used, providing the most effective conditions evaluation order. Secondly we propose a new scanning technique that moves on a 2x2 pixel grid over the image, which is optimized by the automatically generated decision tree.An extensive comparison with the state of art approaches is proposed, both on synthetic and real datasets. The synthetic dataset is composed of different sizes and densities random images, while the real datasets are an artistic image analysis dataset, a document analysis dataset for text detection and recognition, and finally a standard resolution dataset for picture segmentation tasks. The algorithm provides an impressive speedup over the state of the art algorithms.

People social interaction analysis is a complex and interesting problem that can be faced from several points of view depending on the application context. In videosurveillance contexts many indicators of people habits and relations exist and, among these, people trajectories analysis can reveal many aspects of the way people behave in social environments. We propose a statistical framework for trajectories mining that analyzes, in an integrated solution, several aspects of the trajectories such as location, shape and speed properties. Three different models are proposed to deal with non-idealities of the selected features in conjunction with a robust inexact- matching similarity measure for comparing sequences with different lengths. Experimental results in a real scenario demonstrates the efficacy of the framework in clustering people trajectories with the purpose of analyze frequent behaviors in complex environments.

Contextual information can be used both to reduce computationsand to increase accuracy and this paper presentshow it can be exploited for people surveillance in terms ofperspective (i.e. weak scene calibration) and appearance ofthe objects of interest (i.e. relevance feedback on the trainingof a classifier). These techniques are applied to a pedestriandetector that exploits covariance descriptors througha LogitBoost classifier on Riemannian manifolds. The approachhas been tested on a construction working site wherecomplexity and dynamics are very high, making human detectiona real challenge. The experimental results demonstratethe improvements achieved by the proposed approach.

The use of polar representation of covariance descriptors, suitable for the classification of circular feature sets, is proposed. It overcomes the implicit limits of state-of-the-art methods based on axis-oriented rectangular patches. The suitability of the proposed solution is verified on two case studies, namely head detection and polymer classification in photomicrograph contexts.

This paper describes an interactive application for the exploration and annotation of illuminated manuscripts, which typically contain thousands of pictures, used to comment or embellish the manuscript Gothic text. The system is composed by a modern user interface for browsing, surfing and querying, an automatic segmentation module, to ease the initial picture extraction task, and a similarity based retrieval engine, used to provide visually assisted tagging capabilities. A relevance feedback procedure is included to further refine the results.

This paper describes a complete architecture for the interactive exploration and annotation of artistic collections. In particular the focus is on Renaissance illuminated manuscripts, which typically contain thousands of pictures, used to comment or embellish the manuscript Gothic text. The final aim is to create a human centered multimedia application allowing the non practitioners to enjoy these masterpieces and expert users to share their knowledge. The system is composed by a modern user interface for browsing, surfing and querying, an automatic segmentation module, to ease the initial picture extraction task, and a similarity based retrieval engine, used to provide visually assisted tagging capabilities. A relevance feedback procedure is included to further refine the results. Experiments are reported regarding the adopted visual features based on covariance matrices and the Mean Shift Feature Space Warping relevance feedback. Finally some hints on the user interface for museum installations are discussed.

Pattern recognition is becoming a key application in bodyarea networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with patter-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions.

In questo articolo si parla delle nuove frontiere di visione artificiale nella videosorveglianza di persone in ambienti pubblici e privati ed in particolare di analisi comportamentale. Sono poi presentate alcuni progetti in corso presso l’ImageLab di Modena

The availability of new techniques and tools for Video Surveillance and the capability of storing huge amounts of visual data acquired by hundreds of cameras every day call for a convergence between pattern recognition, computer vision and multimedia paradigms. A clear need for this convergence is shown by new research projects which attempt to exploit both ontology-based retrieval and video analysis techniques also in the field of surveillance.This paper presents the ViSOR (Video Surveillance Online Repository) framework, designed with the aim of establishing an open platform for collecting, annotating, retrieving, and sharing surveillance videos, as well as evaluating the performance of automatic surveillance systems. Annotations are based on a reference ontology which has been defined integrating hundreds of concepts, some of them coming from the LSCOM and MediaMill ontologies. A new annotation classification schema is also provided, which is aimed at identifying the spatial, temporal and domain detail level used.The ViSOR web interface allows video browsing, querying by annotated concepts or by keywords, compressed video previewing, media downloading and uploading.Finally, ViSOR includes a performance evaluation desk which can be used to compare different annotations.