Publications by Roberto Vezzani

What approach or combination of approaches did you test in each of your submitted runs?TRECVID2005_UNIMORE_??.xml: the same linear transition detector (LTD) was tested forevery run, with ten uniformly spaced thresholds for the detection.What if any significant differences (in terms of what measures) did you find among theruns?The system behaved as expected: the higher the threshold the better the recall. Of course theprecision lowered correspondently. Interesting enough, it seems that we cannot overcome theoverall limit around 80% for recall and 88% for precision, independently of the other parameter.Based on the results, can you estimate the relative contribution of each component of yoursystem/approach to its effectiveness?One of the main objective of our system was to test the performance of a single algorithm forboth cuts and gradual transitions. So all the merit and the demerits are related to our LTD.Overall, what did you learn about runs/approaches and the research question(s) thatmotivated them?The use of a single algorithm allows the system to be run without training. Just a singleparameter may be employed to tune the sensibility of the system, thus allowing its use in generalpurpose/user friendly systems.

A system for the automatic creation of Pictorially Enriched Ontologies is presented, that is ontologies for context-based video digital libraries, enriched by pictorial concepts for video annotation, summarization and similarity-based retrieval. Extraction of pictorial concepts with video clips clustering, ontology storing with MPEG-7, and the use of the ontology for stored video annotation are described. Re-sults on sport videos and TRECVID2005 video material are reported.

In this paper an advanced video surveillance system is proposed.Our goal is the detection of the people’s heads toallow their obscuration for privacy issues or to performrecognition tasks. We propose a system based on active PTZ(Pan-Tilt-Zoom) cameras that produce head images havinga large enough size, and can cover an area larger than stillcameras. Since conventional approaches are not suitable toPTZ cameras, the proposed approach is based on the socalleddirection histograms to compute the ego-motion andon frame differencing for detecting moving objects. It exploitspost-processing and active contours to extract preciseshape of moving objects to be fed to a probabilistic algorithmto track moving people in the scene. Person following,instead, is based on simple heuristic rules that movethe camera as soon as the selected person is close to theborder of the field of view. Finally, a color and shape basedhead detection that takes advantage of the people trackingis presented. Experimental results on a live active camerademonstrate the feasibility of real-time person followingand of the consecutive head detection phase.

In-house video surveillance to control the safety of people living in domestic environments is considered. In this context, common problems and general purpose computer vision techniques are discussed and implemented in an integrated solution comprising a robust moving object detection module which is able to disregard shadows, a tracking module designed to handle large occlusions, and a posture detector. These factors, shadows, large occlusions and people's posture, are the key problems that are encountered with in-house surveillance systems, A distributed system with cameras installed in each room of a house can be used to provide full coverage of people's movements. Tracking is based on a probabilistic approach in which the appearance and probability of occlusions are computed for the current camera and warped in the next camera's view by positioning the cameras to disambiguate the occlusions. The application context is the emerging area of domotics (from the Latin word domus, meaning 'home', and informatics). In particular, indoor video surveillance, which makes it possible for elderly and disabled people to live with a sufficient degree of autonomy, via interaction with this new technology, which can be distributed in a house at affordable costs and with high reliability.

Posture classification is a key process for analyzing thepeople’s behaviour. Computer vision techniques can behelpful in automating this process, but clutteredenvironments and consequent occlusions make this taskoften difficult. Different views provided by multiplecameras can be exploited to solve occlusions by warpingknown object appearance into the occluded view. To thisaim, this paper describes an approach to postureclassification based on projection histograms, reinforcedby HMM for assuring temporal coherence of the posture.The single camera posture classification is then exploitedin the multi-camera system to solve the cases in which theocclusions make the classification impossible.Experimental results of the classification from both thesingle camera and the multi-camera system are provided.

In this paper, we address the exploitation of computervision techniques to develop multimedia services andautomatic monitoring systems related to the securityand the privacy in public areas. The research is part ofa two-year ltalian project called LAICA, intended toprovide advanced services for citizens and publicofficers. Citizens want fast and friendly web access topublic places, to see the environment in real-timewithout violating the privacy laws. Public officers andpolicy centres want a fast and reactive monitoringsystem, capable to automatically detect dangeroussituations, given the huge amount of cameras that cannot be monitored simultaneously by human operators.In this work, we describe the project and the definedmethodologies in multi-camera video mosaicing,people tracking and consistent labelling, and access toprocessed data with face obscuration.

To enhance video surveillance systems, multi-modal sensorintegration can be a successful strategy. In this work, a computervision system able to detect and track people frommultiple cameras is integrated with a wireless sensor networkmounting PIR (Passive InfraRed) sensors. The twosubsystems are briefly described and possible cases in whichcomputer vision algorithms are likely to fail are discussed.Then, simple but reliable outputs from the PIR sensor nodesare exploited to improve the accuracy of the vision system.In particular, two case studies are reported: the first usesthe presence detection of PIR sensors to disambiguate betweenan opened door and a moving person, while the secondhandles motion direction changes during occlusions. Preliminaryresults are reported and demonstrate the usefulness ofthe integration of the two subsystems.

In this paper we present a people posture classificationapproach especially devoted to cope with occlusions. Inparticular, the approach aims at assessing temporal coherenceof visual data over probabilistic models. A mixed predictiveand probabilistic tracking is proposed: a probabilistictracking maintains along time the actual appearance ofdetected people and evaluates the occlusion probability; anadditional tracking with Kalman prediction improves the estimationof the people position inside the room. ProbabilisticProjection Maps (PPMs) created with a learning phaseare matched against the appearance mask of the track. Finally,an Hidden Markov Model formulation of the posturecorrects the frame-by-frame classification uncertainties andmakes the system reliable even in presence of occlusions.Results obtained over real indoor sequences are discussed.

Video surveillance has a direct application in intelligent home automation or domotics (from the Latin word domus, that means “home”, and informatics). In particular, in-house video surveillance can provide good support for people with some difficulties (e.g. elderly or disabled people) living alone and with limited autonomy. A key aspect in video surveillance systems for domotics is that of analyzing behaviours of the monitored people. To accomplish this task, people must be detected and tracked, and their posture must be analyzed in order to model behaviours recognizing abrupt changes in it. Problems related to reliable software solutions are not completely solved, in particular luminance changes, shadows and frequent posture changes must be taken into account. Long-lasting occlusions are common due to the proximity of the cameras and the presence of furniture and doors that can often hide parts of a person’s body. For these reasons, a probabilistic and appearance-based tracking, particularly conceivable for people tracking and posture classification, has been developed. However, despite its effectiveness for long-lasting and large occlusions, this approach tends to fail whenever the person is monitored with multiple cameras and he appears in one of them already occluded. Different views provided by multiple cameras can be exploited to solve occlusions by warping known object appearance into the occluded view. To this aim, this paper describes an approach to posture classification based on projection histograms, reinforced by HMM for assuring temporal coherence of the posture.

Efficient solution to people tracking in distributed videosurveillance is requested to monitor crowded and large environments.This paper proposes a novel use of the EntryEdges of Field of View (E2oFoV) to solve the consistentlabeling problem between partially overlapped views. Anautomatic and reliable procedure allows to obtain the homographictransformation between two overlapped views,without any manual calibration of the cameras. Throughthe homography, the consistent labeling is established eachtime a new track is detected in one of the cameras. A CameraTransition Graph (CTG) is defined to speed up the establishmentprocess by reducing the search space. Experimentalresults prove the effectiveness of the proposed solutionalso in challenging conditions.