Publications

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.

Smoke detection calls for a reliable and fast distinction between background, moving objects and variable shapes that are recognizable as smoke. In our system we propose a stable background suppression module joined with a smoke detection module working on segmented objects. It exploits two features: the energy variation in wavelet model and a color model of the smoke. The decrease of energy ratio in wavelet domain between background and current image is a clue to detect smoke representing the variations of texture level. A mixture of Gaussians models this texture ratio for temporal evolution. The color model is used as reference to measure the deviation of the current pixel color from the model. The two features have been combined using a Bayesian classifier to detect smoke in the scene. Experiments on real data and a comparison between our background model and Gaussian Mixture(MOG) model for smoke detection are presented. © 2008 IEEE.

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.

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 analysis of patterns of movement is a crucial task for several surveillance applications, for instance to classify normal or abnormal people trajectories on the basis of their occurrence. This paper proposes to model the shape of a single trajectory as a sequence of angles described using a Mixture of Von Mises (MoVM) distribution. A complete EM (Expectation Maximization) algorithm is derived for MoVM parameters estimation and an on-line version proposed to meet real time requirement. Maximum-A-Posteriori is used to encode the trajectory as a sequence of symbols corresponding to the MoVM components. Iterative k-medoids clustering groups trajectories in a variable number of similarity classes. The similarity is computed aligning (with dynamic programming) two sequences and considering as symbol-to-symbol distance the Bhattacharyya distance between von Mises distributions. Extensive experiments have been performed on both synthetic and real data. ©2008 IEEE.

Object tracking with freely moving cameras is an openissue, since background information cannot be exploited forforeground segmentation, and plain feature tracking is notrobust enough for target tracking, due to occlusions, distractors and object deformations. In order to deal withsuch challenging conditions a traditional approach, basedon Camshift-like color-based features, is augmented by introducing a structural model of the object to be tracked incorporating previous knowledge about the spatial relationsbetween the parts. Hence, an attributed graph is built ontop of the features extracted from each frame and a graphmatching technique is used to extract the optimal matchwith the model. Pixel-wise and object-wise comparisonwith other tracking techniques with respect to manually obtained ground truth are presented.

Mobile video surveillance represents a new paradigm that encompasses, on the one side, ubiquitous video acquisition and, on the other side, ubiquitous video processing and viewing, addressing both computer-based and human-based surveillance. To this aim, systems must provide efficient video streaming with low latency and low frame skipping, even over limited bandwidth networks. This work presents MoSES (MObile Streaming for vidEo Surveillance), an effective system for mobile video surveillance for both PC and PDA clients; it relies over H.264/AVC video coding and GPRS/EDGE-GPRS network. Adaptive control algorithms are employed to achieve the best tradeoff between low latency and good video fluidity. MoSES provides a good-quality video streaming that is used as input to computer-based video surveillance applications for people segmentation and tracking. In this paper new and general-purpose methodologies for streaming performance evaluation are also proposed and used to compare MoSES with existing solutions in terms of different parameters (latency, image quality, video fluidity, and frame losses), as well as in terms of performance in people segmentation and tracking.

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.