Publications by Rita Cucchiara

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.

Distributed surveillance systems are nowadays widely adopted to monitor large areas for security purposes. In this paper, we present a complete multicamera system designed for people tracking from multiple partially overlapped views and capable of inferring and detecting abnormal people trajectories. Detection and tracking are performed by means of background suppression and an appearance-based probabilistic approach. Objects' label ambiguities are geometrically solved and the concept of "normality" is learned from data using a robust statistical model based on Von Mises distributions. Abnormal trajectories are detected using a first-order Bayesian network and, for each abnormal event, the appearance of the subject from each view is logged. Experiments demonstrate that our system can process with real-time performance up to three cameras simultaneously in an unsupervised setup and under varying environmental conditions.

This paper proposes the exploitation of a dynamic programming technique for efficiently comparing people trajectories adopting an encoding scheme that jointly takes into account both the direction and the velocity of movement. With this approach, each pair of trajectories in the training set is compared and the corresponding distance computed. Clustering is achieved by using the k-medoids algorithm and each cluster is modeled with a 1-D Gaussian over the distance from the medoid. A MAP framework is adopted for the testing phase. The reported results are encouraging.

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.

This paper presents a open-source system for low-latency video streaming on PDAs, specifically addressing mobile video surveillance requirements. The system is based on H.264 and suitably modified to obtain the best trade-off between image quality and video fluidity, working also at very limited bandwidths. Moreover, the used con- trols allow to keep the number of lost frames very low. A large set of experiments and comparisons have been carried out and the achieved results demonstrate the efficacy and efficiency of our system.

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.

This paper proposes the use of a mixture of Von Mises distributions to detect abnormal behaviors of moving people. The mixture is created from an unsupervised training set by exploiting k-medoids clustering algorithm based on Bhattacharyya distance between distributions. The extracted medoids are used as modes in the multi-modal mixture whose weights are the priors of the specific medoid. Given the mixture model a new trajectory is verified on the model by considering each direction composing it as independent. Experiments over a real scenario composed of multiple, partially-overlapped cameras are reported.

In this paper, we present the dynamic pictorial ontology paradigm for video annotation. Ontologies are often used to describe a given domain for different goals, including description of multimedia data. In the case of video annotation, the visual knowledge cannot be described using only abstract concepts but is more effectively represented in a visual form. To this aim, we introduce visual concepts, elicited from the data set as the most representative prototypes that specialize abstract concepts. The ontology created is intrinsically dynamic since it must embrace the perceptual and visual experience during annotation. Thus visual concepts can change, adapting to the multimedia content analyzed. Motivation for this new ontology paradigm are discussed together with a proposal of a framework for ontology creation, maintenance, and automatic annotation of video. The creation and usage of dynamic pictorial ontologies have been tested for soccer domain exploiting low level perceptual features and higher level domain features.

The paper presents the design and implementation of a system of autonomous navigation of a mobile robot with stereo vision. © 2007 IEEE.