Ubiquitous robust communications for emergency response using multi-operator heterogeneous networks

A number of disasters in various places of the planet have caused an extensive loss of lives, severe damages to properties and the environment, as well as a tremendous shock to the survivors. For relief and mitigation operations, emergency responders are immediately dispatched to the disaster areas. Ubiquitous and robust communications during the emergency response operations are of paramount importance. Nevertheless, various reports have highlighted that after many devastating events, the current technologies used, failed to support the mission critical communications, resulting in further loss of lives. Inefficiencies of the current communications used for emergency response include lack of technology inter-operability between different jurisdictions, and high vulnerability due to their centralized infrastructure. In this article, we propose a flexible network architecture that provides a common networking platform for heterogeneous multi-operator networks, for interoperation in case of emergencies. A wireless mesh network is the main part of the proposed architecture and this provides a back-up network in case of emergencies. We first describe the shortcomings and limitations of the current technologies, and then we address issues related to the applications and functionalities a future emergency response network should support. Furthermore, we describe the necessary requirements for a flexible, secure, robust, and QoS-aware emergency response multi-operator architecture, and then we suggest several schemes that can be adopted by our proposed architecture to meet those requirements. In addition, we suggest several methods for the re-tasking of communication means owned by independent individuals to provide support during emergencies. In order to investigate the feasibility of multimedia transmission over a wireless mesh network, we measured the performance of a video streaming application in a real wireless metropolitan multi-radio mesh network, showing that the mesh network can meet the requirements for high quality video transmissions

Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia

International audienceThis paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients' houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach

Robust Face Detection Based on Convolutional Neural Networks

Automatic face detection in digital video is becoming a very important research topic, due to its wide range of applications, such as security access control, model-based video coding or content-based video indexing. In this paper, we present a connectionist approach for detecting and precisely localizing semi-frontal human faces in complex images, making no assumption on the content or the lighting conditions of the scene, neither on the size, the orientation, and the appearance of the faces. Unlike other systems depending on a hand-crafted feature detection stage, followed by a feature classification stage, we propose a convolutional neural network architecture designed to recognize strongly variable face patterns directly from pixel images with no preprocessing, by automatically synthesizing its own set of feature extractors from a large training set of faces. Moreover, the use of receptive fields, shared weights and spatial subsampling in such a neural model provides some degrees of invariance to translation, rotation, scale, and deformation of the face patterns. We present in details the optimized design of our architecture and our learning strategy. Then, we present the process of face detection using this architecture. Finally, we provide experimental results to demonstrate the robustness of our approach and its capability to precisely detect extremely variable faces in uncontrolled environment

Audiovisual fusion with segment models for video structure analysis

Hidden Markov Models provide a powerful framework for bridging the semantic gap between low-level video features and high-level user needs by taking full advantage of our prior knowledge on the video structure. A serious flaw of HMMs is that they require all the modalities of a video document to be strictly synchronous before their fusion. Taking as a case study tennis broadcasts analysis, we introduce video indexing using Segment Models, a generalization of Hidden Markov Models, where the fusion of different modalities can be performed in a more flexible way. Operating essentially as a layered topology they allow the fusion of asynchronous modalities but do not rely on synchronization points fixed a priori. They also facilitate the fusion of audio models of high-level semantics, like the content of a complete scene, on top of the raw lowlevel audio frames. Segment Models provide encouraging experimental results.

Gender Classification of Faces using Adaboost

Abstract. In this work it is described a framework for classifying face images using Adaboost and domain-partitioning based classifiers. The most interesting aspect of this framework is the capability of building classification systems with high accuracy in dynamical environments, which achieve, at the same time, high processing and training speed. We apply this framework to the specific problem of gender classification. We built several gender classification systems under the proposed framework using different features (LBP, wavelets, rectangular, etc.). These systems are analyzed and evaluated using standard face databases (FERET and BioID), and a new gender classification database of real-world images.