Embedded security system for multi-modal surveillance in a railway carriage
| Autor: | Francois Capman, Hamid Benhadda, Stéphanie Joudrier, Rhalem Zouaoui, David Sodoyer, Romaric Audigier, Sébastien Ambellouis, Thierry Lamarque |
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| Přispěvatelé: | Cadic, Ifsttar, Thales Research and Technology [Palaiseau], THALES [France], Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire Électronique Ondes et Signaux pour les Transports (IFSTTAR/COSYS/LEOST), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-PRES Université Lille Nord de France, Thales Communications [Gennevilliers], Thales Communications, DEGIV, THALES, Laboratoire d'Intégration des Systèmes et des Technologies (LIST) |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Engineering
TRIDIMENSIONNEL 02 engineering and technology Intrusion detection system CAMERA Constant false alarm rate DETECTION PIETON LOCALISATION 0502 economics and business 0202 electrical engineering electronic engineering information engineering Computer vision Cluster analysis TRAITEMENT DES IMAGES [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing RECONNAISSANCE 050210 logistics & transportation RECONNAISSANCE DE FORME Event (computing) business.industry 05 social sciences Video processing 16. Peace & justice Sensor fusion TRANSPORT FERROVIAIRE Analytics Audio analyzer SYSTEME DE MESURE EMBARQUE 020201 artificial intelligence & image processing Artificial intelligence RECONSTRUCTION 3D business [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | SPIE EUROPE SECURITY + DEFENCE SPIE EUROPE SECURITY + DEFENCE, Sep 2015, Toulouse, France. 16p |
| Popis: | SPIE EUROPE SECURITY + DEFENCE , Toulouse, France, 21-/09/2015 - 24/09/2015; Public transport security is one of the main priorities of the public authorities when fighting against crimes and terrorism. In this context, there is a great demand for autonomous systems able to detect abnormal events such as violent acts aboard coaches and intrusions when the train is parked at the depot. To this end, we present an innovative approach which aims at providing efficient automatic event detection by fusing video and audio analytics and reduce the false alarm rate compared to classical video detection. The multi-modal system is composed of two microphones and one camera and integrates onboard video and audio analytics and fusion capabilities. On the one hand, for detecting intrusion, the system relies on the fusion of 'unusual' audio events detection with intrusion detections from video processing. The audio analysis consists in modeling the normal ambience, and detecting deviation from the trained models during testing. This unsupervised approach is based on clustering of automatically extracted segments of acoustic features and statistical GMM modeling of each cluster. The intrusion detection is based on the 3D detection and tracking of individuals in the videos. On the other hand, for violent events detection, the system fuses unsupervised and supervised audio algorithms with video event detection. The supervised audio technique detects specific events such as shouts. A Gaussian Mixture Model is used to catch the formantic structure of a shout signal. Video analytics use an original approach for detecting aggressive motion by focusing on erratic motion patterns specific to violent events. As data with violent events is not easily available, a normality model with structured motions from non-violent videos is learned for one-class classification. A fusion algorithm based on Dempster-Shafer's theory analyses the asynchronous detection outputs and computes the degree of belief of each probable event. |
| Databáze: | OpenAIRE |
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