Towards a semi-automatic situation diagnosis system in surveillance tasks

This paper describes an ongoing project that develops a set of generic components to help humans (semi-automatic system) in surveillance and security tasks in several scenarios. These components are based in the computational model of a set of selective and Active VISual Attention mechanisms with learning capacity (AVISA) and in the superposition of an ?intelligence? layer that incorporates the knowledge of human experts in security tasks. The project described integrates the responses of these alert mechanisms in the synthesis of the three basic subtasks present in any surveillance and security activity: real-time monitoring, situation diagnosing, and action planning and control. In order to augment the diversity of environments and situations where AVISA system may be used, as well as its efficiency as support to surveillance tasks, knowledge components derived from situating cameras on mobile platforms are also developed

The Investigative Factors in Whodunit Homicides: Italian Case

Gli studi sulla risolvibilità dei casi di omicidio si sono tradizionalmente focalizzati sull'esame dei fattori relativi alle caratteristiche delle vittime o degli autori oppure sulle circostanze che caratterizzano l’evento omicidiario. Vi è infatti una certa carenza di ricerche finalizzate ad accertare la potenziale influenza dei fattori legati al processo investigativo sul positivo esito delle indagini. Ciò è dovuto principalmente alle difficoltà legate all'ottenimento dei dati necessari, che non possono essere recuperati nelle banche dati di polizia su cui tali studi si basano abitualmente, ma possono essere ottenuti solo attraverso la cooperazione degli investigatori stessi. Attraverso la distribuzione di un sondaggio a quasi un centinaio di investigatori appartenenti all’Arma dei Carabinieri, questa ricerca ha identificato una serie di fattori investigativi alcuni dei quali sono risultati positivamente correlati con la soluzione dei casi di omicidio trattati. Più specificamente, i risultati emersi dall'analisi statistica descrittiva e inferenziale hanno corroborato le ipotesi di partenza, secondo cui l'implementazione di alcune buone pratiche associate ad un’efficace gestione delle risorse umane, alla scrupolosa esecuzione delle attività condotte sulla scena del crimine, nonché ad alcune specifiche strategie e tecniche investigative possono aumentare significativamente la soluzione dei casi. Il presente studio contribuisce al dibattito accademico in primo luogo introducendo un approccio olistico attraverso il quale valutare l'effetto dei fattori investigativi sulla risoluzione di quei casi di omicidio che richiedono un certo livello di sforzo investigativo da parte delle forze di polizia e, in secondo luogo, proponendo alcune innovative prospettive attraverso le quali superare i limiti della letteratura esistente.Research on homicide clearance has traditionally focussed on examining factors pertaining to the characteristics of the victims or perpetrators or the circumstances surrounding the murder. There has been a relative dearth of research addressing the potential influence of investigative factors on the positive outcomes of murder investigations. This was primarily due to the difficulties involved in obtaining the requisite data, which cannot be found in the police databases that such studies routinely rely on, but rather can only be obtained via the cooperation of detectives themselves. Through administering a survey to almost one-hundred Italian Carabinieri homicide detectives, this research identified a number of investigative factors that have been observed in previous studies, of which some were found to be positively correlated with clearance. More specifically, the findings emerging out of the descriptive and inferential statistical analysis conducted for the purposes of this research corroborated the study’s hypotheses, which posited that the implementation of certain best practices associated with human resource management, crime scene activities, investigative strategies and techniques can positively impact upon homicide clearance. The present study contributes to academic debates on homicide clearance, firstly, by introducing a holistic approach through which to evaluate the effect of investigative factors on solving those murder cases which require a certain level of investigative effort on the behalf of the police, and secondly, by presenting avenues through which to overcome the limitations in extant literature

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Toward knowledge-based automatic 3D spatial topological modeling from LiDAR point clouds for urban areas

Le traitement d'un très grand nombre de données LiDAR demeure très coûteux et nécessite des approches de modélisation 3D automatisée. De plus, les nuages de points incomplets causés par l'occlusion et la densité ainsi que les incertitudes liées au traitement des données LiDAR compliquent la création automatique de modèles 3D enrichis sémantiquement. Ce travail de recherche vise à développer de nouvelles solutions pour la création automatique de modèles géométriques 3D complets avec des étiquettes sémantiques à partir de nuages de points incomplets. Un cadre intégrant la connaissance des objets à la modélisation 3D est proposé pour améliorer la complétude des modèles géométriques 3D en utilisant un raisonnement qualitatif basé sur les informations sémantiques des objets et de leurs composants, leurs relations géométriques et spatiales. De plus, nous visons à tirer parti de la connaissance qualitative des objets en reconnaissance automatique des objets et à la création de modèles géométriques 3D complets à partir de nuages de points incomplets. Pour atteindre cet objectif, plusieurs solutions sont proposées pour la segmentation automatique, l'identification des relations topologiques entre les composants de l'objet, la reconnaissance des caractéristiques et la création de modèles géométriques 3D complets. (1) Des solutions d'apprentissage automatique ont été proposées pour la segmentation sémantique automatique et la segmentation de type CAO afin de segmenter des objets aux structures complexes. (2) Nous avons proposé un algorithme pour identifier efficacement les relations topologiques entre les composants d'objet extraits des nuages de points afin d'assembler un modèle de Représentation Frontière. (3) L'intégration des connaissances sur les objets et la reconnaissance des caractéristiques a été développée pour inférer automatiquement les étiquettes sémantiques des objets et de leurs composants. Afin de traiter les informations incertitudes, une solution de raisonnement automatique incertain, basée sur des règles représentant la connaissance, a été développée pour reconnaître les composants du bâtiment à partir d'informations incertaines extraites des nuages de points. (4) Une méthode heuristique pour la création de modèles géométriques 3D complets a été conçue en utilisant les connaissances relatives aux bâtiments, les informations géométriques et topologiques des composants du bâtiment et les informations sémantiques obtenues à partir de la reconnaissance des caractéristiques. Enfin, le cadre proposé pour améliorer la modélisation 3D automatique à partir de nuages de points de zones urbaines a été validé par une étude de cas visant à créer un modèle de bâtiment 3D complet. L'expérimentation démontre que l'intégration des connaissances dans les étapes de la modélisation 3D est efficace pour créer un modèle de construction complet à partir de nuages de points incomplets.The processing of a very large set of LiDAR data is very costly and necessitates automatic 3D modeling approaches. In addition, incomplete point clouds caused by occlusion and uneven density and the uncertainties in the processing of LiDAR data make it difficult to automatic creation of semantically enriched 3D models. This research work aims at developing new solutions for the automatic creation of complete 3D geometric models with semantic labels from incomplete point clouds. A framework integrating knowledge about objects in urban scenes into 3D modeling is proposed for improving the completeness of 3D geometric models using qualitative reasoning based on semantic information of objects and their components, their geometric and spatial relations. Moreover, we aim at taking advantage of the qualitative knowledge of objects in automatic feature recognition and further in the creation of complete 3D geometric models from incomplete point clouds. To achieve this goal, several algorithms are proposed for automatic segmentation, the identification of the topological relations between object components, feature recognition and the creation of complete 3D geometric models. (1) Machine learning solutions have been proposed for automatic semantic segmentation and CAD-like segmentation to segment objects with complex structures. (2) We proposed an algorithm to efficiently identify topological relationships between object components extracted from point clouds to assemble a Boundary Representation model. (3) The integration of object knowledge and feature recognition has been developed to automatically obtain semantic labels of objects and their components. In order to deal with uncertain information, a rule-based automatic uncertain reasoning solution was developed to recognize building components from uncertain information extracted from point clouds. (4) A heuristic method for creating complete 3D geometric models was designed using building knowledge, geometric and topological relations of building components, and semantic information obtained from feature recognition. Finally, the proposed framework for improving automatic 3D modeling from point clouds of urban areas has been validated by a case study aimed at creating a complete 3D building model. Experiments demonstrate that the integration of knowledge into the steps of 3D modeling is effective in creating a complete building model from incomplete point clouds