Publications

This paper presents the Visor (VIdeo Surveillance Online Repository) project designed with the aim of establishing anopen platform for collecting, annotating, retrieving, sharingsurveillance videos, and of evaluating the performanceof automatic surveillance systems. The main idea is to exploitthe collaborative paradigm spreading in the web communityto join together the ontology based annotation andretrieval concepts and the requirements of the computer visionand video surveillance communities. The ViSOR openrepository is based on a reference ontology which integratesmany concepts, also coming from LSCOM and MediaMillontologies. The web interface allows video browse, queryby annotated concepts or by keywords, compressed videopreview, media download and upload. The repository containsmetadata annotations, which can be either manuallycreated as ground truth or automatically generated by videosurveillance systems. Their automatic annotations can becompared each other or with the reference ground-truth exploitingan integrated on-line performance evaluator.

Visor (VIdeo Surveillance Online Repository) is a large videorepository, designed for containing annotated video surveillancefootages, comparing annotations, evaluating systemperformance, and performing retrieval tasks. The web interfaceallows video browse, query by annotated conceptsor by keywords, compressed video preview, media downloadand upload. The repository contains metadata annotations,both manually created ground-truth data and automaticallyobtained outputs of particular systems. An exampleof application is the collection of videos and annotationsfor smoke detection, an important video surveillance task. Inthis paper we present the architecture of ViSOR, the build-insurveillance ontology which integrates many concepts, alsocoming from LSCOM, and MediaMill, the annotation toolsand the visualization of results for performance evaluation.The annotation is obtained with an automatic smoke detectionsystem, capable to detect people, moving objects, andsmoke in real-time.

The Imagelab Laboratory of the University of Modena andReggio Emilia has designed a large video repository, aimingat containing annotated video surveillance footages. The webinterface, named ViSOR (VIdeo Surveillance Online Repository),allows video browse, query by annotated concepts or bykeywords, compressed preview, video download and upload.The repository contains metadata annotation, both manuallyannotated ground-truth data and automatically obtained outputsof a particular system. In such a manner, the users of therepository are able to perform validation tasks of their ownalgorithms as well as comparative activities.

In this paper we present a system for automatic segmentation, annotation and image retrieval based on content, focused on illuminated manuscripts and in particular the Borso D'Este Holy Bible. To enhance the interaction possibilities with this work, full of decorations and illustrations, we exploit some well known document analysis techniques in addition to some new approaches, in order to achieve good segmentation of pages into meaningful visual objects with the relative annotation. We wanted to extend the standard keyword-based retrieval approach in a commentary with a modern visual-based retrieval by appearance similarity: an entire software user interface for exploration and visual search of illuminated manuscripts.

Recognizing different actions with a unique approach can be a difficult task. This paper proposes a novel holistic representation of actions that we called "action signature". This 1D trajectory is obtained by parsing the 2D image containing the orientations of the gradient calculated on the motion feature map called motion-history image. In this way, the trajectory is a sketch representation of how the object motion varies in time. A robust statistical framework based on mixtures of von Mises distributions and dynamic programming for sequence alignment are used to compare and classify actions/trajectories. The experimental results show a rather high accuracy in distinguishing quite complicated actions, such as drinking, jumping, or abandoning an object.

This paper presents a novel and robust approach to consistent labeling for people surveillance in multi-camera systems. A general framework scalable to any number of cameras with overlapped views is devised. An off-line training process automatically computes ground-plane homography and recovers epipolar geometry. When a new object is detected in any one camera, hypotheses for potential matching objects in the other cameras are established. Each of the hypotheses is evaluated using a prior and likelihood value. The prior accounts for the positions of the potential matching objects, while the likelihood is computed by warping the vertical axis of the new object on the field of view of the other cameras and measuring the amount of match. In the likelihood, two contributions (forward and backward) are considered so as to correctly handle the case of groups of people merged into single objects. Eventually, a maximum-a-posteriori approach estimates the best label assignment for the new object. Comparisons with other methods based on homography and extensive outdoor experiments demonstrate that the proposed approach is accurate and robust in coping with segmentation errors and in disambiguating groups.

The paper presents a new fast and robust technique of smoke detection in video surveillance images. The approach aims at detecting the spring or the presence of smoke by analyzing color and texture features of moving objects, segmented with background subtraction. The proposal embodies some novelties: first the temporal behavior of the smoke is modeled by a Mixture of Gaussians (MoG ) of the energy variation in the wavelet domain. The MoG takes into account the image energy variation due to either external luminance changes or the smoke propagation. It allows a distinction to energy variation due to the presence of real moving objects such as people and vehicles. Second, this textural analysis is enriched by a color analysis based on the blending function. Third, a Bayesian model is defined where the texture and color features, detected at block level, contributes to model the likelihood while a global evaluation of the entire image models the prior probability contribution. The resulting approach is very flexible and can be adopted in conjunction to a whichever video surveillance system based on dynamic background model. Several tests on tens of different contexts, both outdoor and indoor prove its robustness and precision. © 2008 Springer-Verlag Berlin Heidelberg.

Truly ubiquitous computing poses new and significantchallenges. One of the key aspects that will condition theimpact of these new tecnologies is how to obtain a manageablerepresentation of the surrounding environment startingfrom simple sensing capabilities. This will make devicesable to adapt their computing activities on an everchangingenvironment. This paper presents a frameworkto promote unsupervised training processes among differentsensors. This framework allows different sensors to exchangethe needed knowledge to create a model to classifyevents. In particular we developed, as a case study,a multi-modal multi-sensor classification system combiningdata from a camera and a body-worn accelerometer to identifythe user motion state. The body-worn accelerometerlearns a model of the user behavior exploiting the informationcoming from the camera and uses it later on to classifythe user motion in an autonomous way. Experimentsdemonstrate the accuracy of the proposed approach in differentsituations.