Night-time outdoor surveillance with mobile cameras

International audienceThis paper addresses the problem of video surveillance by mobile cameras. We present a method that allows online change detection in night-time outdoor surveillance. Because of the camera movement, background frames are not available and must be "localized" in former sequences and registered with the current frames. To this end, we propose a Frame Localization And Registration (FLAR) approach that solves the problem efficiently. Frames of former sequences define a database which is queried by current frames in turn. To quickly retrieve nearest neighbors, database is indexed through a visual dictionary method based on the SURF descriptor. Furthermore, the frame localization is benefited by a temporal filter that exploits the temporal coherence of videos. Next, the recently proposed ECC alignment scheme is used to spatially register the synchronized frames. Finally, change detection methods apply to aligned frames in order to mark suspicious areas. Experiments with real night sequences recorded by in-vehicle cameras demonstrate the performance of the proposed method and verify its efficiency and effectiveness against other methods

Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literatureThe work by Quijano-Sanchez was supported by the Spanish Ministry of Science and Innovation grant FJCI-2016-28855. The research of Liberatore was supported by the Government of Spain, grant MTM2015-65803-R, and by the European Union’s Horizon 2020 Research and Innovation Programme, under the Marie Sklodowska-Curie grant agreement No. 691161 (GEOSAFE). All the financial support is gratefully acknowledge

Utilization Of A Large-Scale Wireless Sensor Network For Intrusion Detection And Border Surveillance

To control the border more effectively, countries may deploy a detection system that enables real-time surveillance of border integrity. Events such as border crossings need to be monitored in real time so that any border entries can be noted by border security forces and destinations marked for apprehension. Wireless Sensor Networks (WSNs) are promising for border security surveillance because they enable enforcement teams to monitor events in the physical environment. In this work, probabilistic models have been presented to investigate senor development schemes while considering the environmental factors that affect the sensor performance. Simulation studies have been carried out using the OPNET to verify the theoretical analysis and to find an optimal node deployment scheme that is robust and efficient by incorporating geographical coordination in the design. Measures such as adding camera and range-extended antenna to each node have been investigated to improve the system performance. A prototype WSN based surveillance system has been developed to verify the proposed approach

Finalised dependability framework and evaluation results

The ambitious aim of CONNECT is to achieve universal interoperability between heterogeneous Networked Systems by means of on-the-fly synthesis of the CONNECTors through which they communicate. The goal of WP5 within CONNECT is to ensure that the non-functional properties required at each side of the connection going to be established are fulfilled, including dependability, performance, security and trust, or, in one overarching term, CONNECTability. To model such properties, we have introduced the CPMM meta-model which establishes the relevant concepts and their relations, and also includes a Complex Event language to express the behaviour associated with the specified properties. Along the four years of project duration, we have developed approaches for assuring CONNECTability both at synthesis time and at run-time. Within CONNECT architecture, these approaches are supported via the following enablers: the Dependability and Performance analysis Enabler, which is implemented in a modular architecture supporting stochastic verification and state-based analysis. Dependability and performance analysis also relies on approaches for incremental verification to adjust CONNECTor parameters at run-time; the Security Enabler, which implements a Security-by-Contract-with-Trust framework to guarantee the expected security policies and enforce them accordingly to the level of trust; the Trust Manager that implements a model-based approach to mediate between different trust models and ensure interoperable trust management. The enablers have been integrated within the CONNECT architecture, and in particular can interact with the CONNECT event-based monitoring enabler (GLIMPSE Enabler released within WP4) for run-time analysis and verification. To support a Model-driven approach in the interaction with the monitor, we have developed a CPMM editor and a translator from CPMM to the GLIMPSE native language (Drools). In this document that is the final deliverable from WP5 we first present the latest advances in the fourth year concerning CPMM, Dependability&Performance Analysis, Incremental Verification and Security. Then, we make an overall summary of main achievements for the whole project lifecycle. In appendix we also include some relevant articles specifically focussing on CONNECTability that have been prepared in the last period