Human Centered Computer Vision Techniques for Intelligent Video Surveillance Systems

Nowadays, intelligent video surveillance systems are being developed to support human operators in different monitoring and investigation tasks. Although relevant results have been achieved by the research community in several computer vision tasks, some real applications still exhibit several open issues. In this context, this thesis focused on two challenging computer vision tasks: person re-identification and crowd counting. Person re-identification aims to retrieve images of a person of interest, selected by the user, in different locations over time, reducing the time required to the user to analyse all the available videos. Crowd counting consists of estimating the number of people in a given image or video. Both tasks present several complex issues. In this thesis, a challenging video surveillance application scenario is considered in which it is not possible to collect and manually annotate images of a target scene (e.g., when a new camera installation is made by Law Enforcement Agency) to train a supervised model. Two human centered solutions for the above mentioned tasks are then proposed, in which the role of the human operators is fundamental. For person re-identification, the human-in-the-loop approach is proposed, which exploits the operator feedback on retrieved pedestrian images during system operation, to improve system's effectiveness. The proposed solution is based on revisiting relevance feedback algorithms for content-based image retrieval, and on developing a specific feedback protocol, to find a trade-off between the human effort and re-identification performance. For crowd counting, the use of a synthetic training set is proposed to develop a scene-specific model, based on a minimal amount of information of the target scene required to the user. Both solutions are empirically investigated using state-of-the-art supervised models based on Convolutional Neural Network, on benchmark data sets

Inviscid supersonic minimum length nozzle design

An aerospace vehicle is accelerated by a propulsion system to a given velocity. A nozzle is used to extract the maximum thrust from high pressure exhaust gases generated by the propulsion system. The nozzle is responsible for providing the thrust necessary to successfully accomplish the mission while its design efficiency translates to greater payload and reduction in propellant consumption. Specifically, the nozzle is that portion of the engine beyond the combustion chamber. Typically, the combustion chamber is a constant area duct into which propellants are injected, mixed and burned. Its length is sufficient to complete the combustion of the propellant before the nozzle accelerates the gas products. The nozzle is said to begin at the point where the chamber diameter begins to decrease. This paper exploits the De Laval nozzle, a convergent divergent nozzle invented by Carl De Laval toward the end of the 19th century, and it tries to give a practical procedure to design the nozzle of minimum length. The basic assumption made is that the boundary layer thickness is small compared to the characteristic length, i.e. nozzle radius, so that the nozzle flow field can be treated as inviscid for the purpose of designing the aerodynamic lines. Ones the aerodynamic lines are determined, a correction can be made to account for the displacement thickness of the boundary layer. This second step of the designing procedure is not treated in here. This basic procedure has been applied successfully to many supersonic nozzle

<i>Egnatuleius Anastasius</i>: un nuovo <i>praefectus vigilum</i> da Dorgali

Nel sito di Siddai, nel comune di Dorgali (Nuoro), frequentato nel corso di tutta l’età romana, è stato rinvenuto un disco di bronzo con iscrizione menzionante Egnatuleius Anastasius, prefetto dei vigili probabilmente in età costantiniana. Il documento era verosimilmente pertinente a un edificio funzionale la raccolta dei prodotti annonari o a una piccola “stazione” dei vigili, per l’ispezione dei magazzini rurali nell’area di Dorgali

Un Frammento di anfora con iscrizione LEON[---] dall’insediamento di Nuraghe Mannu (Dorgali, Nuoro)

Il complesso archeologico di Nuraghe Mannu è posizionato sopra un terrazzo basaltico (200 m s.l.m. circa) in prossimità del tratto costiero di Cala Gonone, frazione marina di Dorgali. Il sito, indagato per la prima volta da A. Taramelli, è da alcuni anni oggetto delle indagini archeologiche curate dalla Soprintendenza per i Beni Archeologici per le province di Sassari e Nuoro in collaborazione con il Comune di Dorgali

An Empirical Evaluation of Cross-scene Crowd Counting Performance

Crowd counting and density estimation are useful but also challenging tasks in many video surveillance systems, especially in cross-scene settings with dense crowds, if the target scene significantly differs from the ones used for training. Recently, Convolutional Neural Networks (CNNs) have boosted the performance of crowd counting systems, but they require massive amounts of annotated training data. As a consequence, when training data is scarce or not representative of deployment scenarios, also CNNs may suffer from over-fitting to a different extent, and may hardly generalise to images coming from different scenes. In this work we focus on real-world, challenging application scenarios when no annotated crowd images from a given target scene are available, and evaluate the cross-scene effectiveness of several regression-based state-of-the-art methods, including the most recent, CNN-based ones, through extensive cross-data set experiments. Our results show that some of the existing CNN-based approaches are capable of generalising to target scenes which differ from the ones used for training in the background or lighting conditions, whereas their effectiveness considerably degrades under different perspective and scale

L'Insediamento romano di Sant’Efis (Orune, Nuoro), scavi 2004-06: nota preliminare

L’area archeologica di Sant’Efis (località Sant’Efisio), posizionata nel territorio del Comune di Orune (Nuoro), si localizza su un altopiano alberato a circa 750 metri s.l.m.; il complesso è raggiungibile da una deviazione a destra del km 81,900 della Statale 389, nel tratto tra Orune e Nuoro. Il sito comprende il nuraghe complesso di Sant’Efis, una fonte nuragica, il villaggio nuragico e, sovrapposto in parte ad esso, l’insediamento romano, esteso per oltre due ettari; alle fasi più tarde di frequentazione dell’area si può ascrivere la costruzione della chiesa di S. Efisio. A breve distanza dal complesso si localizzano, inoltre, cinque tombe di giganti. Relativamente all'insediamento di Sant'Efis si può ipotizzare che della comunità locale, nella quale un ruolo importante doveva essere rivestito da commercianti e artigiani, facessero parte anche i discendenti, ormai romanizzati, dei populi delle cosiddette civitates Barbariae, la cui esistenza è nota da fonti epigrafiche della prima età imperiale

Scene-specific crowd counting using synthetic training images

Crowd counting is a computer vision task on which considerable progress has recently been made thanks to convolutional neural networks. However, it remains a challenging task even in scene-specific settings, in real-world application scenarios where no representative images of the target scene are available, not even unlabelled, for training or fine-tuning a crowd counting model. Inspired by previous work in other computer vision tasks, we propose a simple but effective solution for the above application scenario, which consists of automatically building a scene-specific training set of synthetic images. Our solution does not require from end-users any manual annotation effort nor the collection of representative images of the target scene. Extensive experiments on several benchmark data sets show that the proposed solution can improve the effectiveness of existing crowd counting methods

Synthetic Data for Video Surveillance Applications of Computer Vision: A Review

In recent years, there has been a growing interest in synthetic data for several computer vision applications, such as automotive, detection and tracking, surveillance, medical image analysis and robotics. Early use of synthetic data was aimed at performing controlled experiments under the analysis by synthesis approach. Currently, synthetic data are mainly used for training computer vision models, especially deep learning ones, to address well-known issues of real data, such as manual annotation effort, data imbalance and bias, and privacy-related restrictions. In this work, we survey the use of synthetic training data focusing on applications related to video surveillance, whose relevance has rapidly increased in the past few years due to their connection to security: crowd counting, object and pedestrian detection and tracking, behaviour analysis, person re-identification and face recognition. Synthetic training data are even more interesting in this kind of application, to address further, specific issues arising, e.g., from typically unconstrained image or video acquisition conditions and cross-scene application scenarios. We categorise and discuss the existing methods for creating synthetic data, analyse the synthetic data sets proposed in the literature for each of the considered applications, and provide an overview of their effectiveness as training data. We finally discuss whether and to what extent the existing synthetic data sets mitigate the issues of real data, highlight existing open issues, and suggest future research directions in this field

Investigating Synthetic Data Sets for Crowd Counting in Cross-scene Scenarios

Crowd counting and density estimation are crucial functionalities in intelligent video surveillance systems but are also very challenging computer vision tasks in scenarios characterised by dense crowds, due to scale and perspective variations, overlapping and occlusions. Regression-based crowd counting models are used for dense crowd scenes, where pedestrian detection is infeasible. We focus on real-world, cross-scene application scenarios where no manually annotated images of the target scene are available for training regression models, but only images with different backgrounds and camera views can be used (e.g., from publicly available data sets), which can lead to low accuracy. To overcome this issue, we propose to build the training set using emph{synthetic} images of the target scene, which can be automatically annotated with no manual effort. This work provides a preliminary empirical evaluation of the effectiveness of the above solution. To this aim, we carry out experiments using real data sets as the target scenes (testing set) and using different kinds of synthetically generated crowd images of the target scenes as training data. Our results show that synthetic training images can be effective, provided that also their background, beside their perspective, closely reproduces the one of the target scene