Publications by Roberto Vezzani

This article presents a framework for automatic semantic annotation of video streams with an ontology that includes concepts expressed using linguistic terms and visual data.

This chapter will address most of the aspects of modern video surveillance with the reference to the research activity conducted at University of Modena and Reggio Emilia, Italy, within the scopes of the national FREE SURF (FREE SUrveillance in a pRivacy-respectFul way) and NATO-funded BE SAFE (Behavioral lEarning in Surveilled Areas with Feature Extraction) projects. Moving object detection and tracking from a single camera, multi-camera consistent labeling and trajectory shape analysis for path classification will be the main topics of this chapter.

AD-HOC copes with the problem of multiple people tracking in video surveillance in presence of large occlusions. The main novelty is the adoption of an appearance-based approach in a formal Bayesian framework: the status of each object is defined at pixel level, where each pixel is characterized by the appearance, i.e. the color (integrated along the time) and the likelihood to belong to the object. With these data at pixel-level and a probability of non-occlusion at object-level, the problem of occlusions is addressed. The method does not aim at detecting the presence of an occlusion only, but classifies the type of occlusion at a sub-region level and evolve the status of theobject in a selective way. The AD-HOC tracking has been tested in many application for indoor and outdoor surveillance. Results on PETS2006 test set are reported where many people and abandoned objects are detected and tracked.

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-house video surveillance can represent an excellent support for people with some difficulties (e.g. elderly or disabled people) living alone and with a limited autonomy. New hardware technologies and in particular digital cameras are now affordable and they have recently gained credit as tools for (semi-)automatically assuring people's safety. In this paper a multi-camera vision system for detecting and tracking people and recognizing dangerous behaviours and events such as a fall is presented. In such a situation a suitable alarm can be sent, e.g. by means of an SMS. A novel technique of warping people's silhouette is proposed to exchange visual information between partially overlapped cameras whenever a camera handover occurs. Finally, a multi-client and multi-threaded transcoding video server delivers live video streams to operators/remote users in order to check the validity of a received alarm. Semantic and event-based transcoding algorithms are used to optimize the bandwidth usage. A two-room setup has been created in our laboratory to test the performance of the overall system and some of the results obtained are reported.

To enhance video surveillance systems, multi-modal sensor integration can be a successful strategy. In this work, a computer vision system able to detect and track people from multiple cameras is integrated with a wireless sensor network mounting passive Pyroelectric InfraRed sensors. Thetwo subsystems are briefly described and possible cases in which computer vision algorithms are likely to fail are discussed. Then, simple but reliable outputs from the sensor nodes are exploited to improve the accuracy of the vision system. In particular, two case studies are reported: the first uses the presence detection of sensors to disambiguate between an open door and a moving person, while the second handles motion direction changes during occlusions. Preliminary results are reported and demonstrate the usefulness of the integration of the two subsystems.

In this work, we propose a system for shot comparison directly working on the MPEG-1 stream in the compressed domain, extracting both color, texture and motion features considering all frames with a reasonable computational cost, and results comparable to those obtained on uncompressed keyframes. In particular a summary descriptor for each Group Of Pictures (GOP) is computed and employed for shot characterization and comparison. The Mallows distance allows to match different length clips in a unified framework.

Color is one of the most meaningful features used in content based retrieval of visual data. In video content based retrieval, color features computed on selected frames are integrated with other low-level features concerning texture, shape and motion in order to find clip similarities. For example, the Scalable Color feature defined in the MPEG-7 standard exploits HSV histograms to create color feature vectors. HSV is a widely adopted space in image and video retrieval, but its quantization for histogram generation can create misleading errors in classification of achromatic and low saturated colors. In this paper we propose an Enhanced HSV Histogram with achromatic point detection based on a single Hue and Saturation parameter that can correct this limitation. The enhanced histograms have proven to be effective in color analysis and they have been used in a system for automatic clip annotation called PEANO, where pictorial concepts are extracted by a clip clustering and used for similarity based automatic annotation.