With the irruption of inexpensive depth sensor devices, hand gesture tracking has become a topic of great interest. Two main problems to face respect other tracking algorithms are the high complexity of the hand structure, which translate in a very large amount of possible gestures, and the rapidness of the movements we are able to make when moving the hand or just the fingers. Recent approaches try to fit a 3D hand model to the observed RGB-D data by an optimization function that minimizes the error between the model and the data. However, these algorithms are very dependent on the initialization point, which are impractical to run in a natural environment. To solve these kinds of problems, it is common to use an offline data set with prelearned gestures that will serve as a first rough estimate. In concrete, we present an algorithm that uses an articulated ICP minimization function that is initialized by the parameters obtained from a data set of hand gestures trained through a deep learning framework. This setup has two strong points. First, deep learning provides a very fast and accurate estimate of performed hand gestures. Second, the articulated ICP algorithm allows capturing the possible variability of a gesture performed by different persons or slightly different gestures. Our proposed algorithm is evaluated and validated in several ways. Independent evaluations for the deep learning framework and articulated ICP are performed. Moreover, different real sequences are recorded to validate our approach and, finally, quantitative and qualitative comparisons are conducted with state-of-the-art algorithms.

A smart city is an interconnected entity of internet of things and intelligent systems to provide qualify services to its citizens in various sectors such as public safety, healthcare, transportation and energy. In order for users to get the provided services, they have to access these services, provide and exchange a lot of personal information in an open, unsafe and interconnected environment. Therefore, user information may be exposed to privacy violations, which is especially critical for healthcare related services. Privacy engineering challenges that are not considered in design and development of these services may lead to possible privacy holes and leakage points, which raise concerns among users. In this paper, a privacy engineering approach and safeguard framework are proposed for smart city healthcare services. The paper aims to understand the privacy holes, identify the privacy leakage points and study privacy requirements to propose a privacy safeguard design solution.

Summary Because of the highly dynamic topology and the unstable service status of nodes, services in vehicular ad hoc networks (VANETs) are not always reliable enough for users. Nodes in such a VANET incline to be selfish, which will even enhance this situation. In this work, we present an incentive model for VANETs to support more reliable services in network. We model the situation of service request and response in VANETs by using game theory. We consider the competitive and cooperative relationship between the nodes to formulate the game for VANETs. A contribution measurement is given in order to encourage cooperation during the game. Nodes are encouraged to provide more services to their neighbors in order to acquire more services from other nodes in our model. We also conduct a simulation for the proposed model and give detailed analysis in this work. From the results of the simulation, we argue that we could enable a VANET to support more reliable service by configuring suitable parameters for it. Copyright © 2014 John Wiley & Sons, Ltd.

Context-aware recommendations offer the potential of exploiting social contents and utilize related tags and rating information to personalize the search for content considering a given context. Recommendation systems tackle the problem of trying to identify relevant resources from the vast number of choices available online. In this study, we propose a new recommendation model that personalizes recommendations and improves the user experience by analyzing the context when a user wishes to access multimedia content. We conducted empirical analysis on a dataset from last.fm to demonstrate the use of latent preferences for ranking items under a given context. Additionally, we use an optimization function to maximize the mean average precision measure of the resulted recommendation. Experimental results show a potential improvement to the quality of the recommendation in terms of accuracy when compared with state-of-the-art algorithms.

Wireless sensor network (WSN) consists of resource constraint sensor nodes where nodes (sensors) send data to the base station/sink node and communicate with each other by either forming a cluster or without forming a cluster. Data aggregation in WSN takes place at the responsible nodes (aggregators) in a cluster before sending data to the base station, based on the query that is received from the base station. Thus data aggregation reduces energy consumption of the nodes due to minimized communication. As a result, the life time of the individual sensors prolong in the case of aggregation compared to the data transmission that occurs without performing aggregation. One of the major security challenges for data aggregation in WSN is that the aggregators expose clear data at the aggregation level. Therefore, this aggregation level is vulnerable to attacks by intruders. Existing research has addressed this problem and proposed solutions by considering static node topology of WSN. However, in WSN the nodes can either be static or dynamic. Therefore, the existing approaches do not tackle the security issues that arise in dynamic node WSN. The proposed research aims to explore this problem and propose solutions based on a cryptographic approach.

Smart city is the vision of future city - with increasingly instrumented, inter-connected and intelligent urban systems - to improve the quality of life in many aspects including public safety, healthcare, transportation, or energy. With the ever-increasing presence of multimodal sensors in the smart city infrastructure, multimedia plays an indispensable role. The proliferation of multimedia, sensors, pervasive devices, and infrastructures for realizing smart city has brought many challenges that are the core focus of EMASC workshop.

The growing number of elderly population at home and abroad necessitates improved approaches to elderly care provision. Elders, often with cognitive and physical impairment, need assistance in their activities of daily living (ADLs), which is usually provided by human caregivers. As the demand for caregivers assistance increases, the shortage of traditional care resources becomes obvious. In this paper, we present the Virtual Caregiver (ViCare) framework that supports a human caregiver to monitor continuously the elderly by being aware of their surroundings. The ViCare system attempts to understand the elderly persons activities and contexts based on the data captured by the sensors placed in their environment and dynamically decides what services to provide them or whether there is a need to interrupt human caregiver depending on the type of activities. It not only minimizes the cognitive load of the human caregiver but also provides a seamless assistance to the elderly toward their improved health and well-being in their living environment. We conducted the experiments in an instrumented home environment and obtained positive results in terms of the satisfaction of the elderly, interaction event handling, caregivers acceptance and their engagement.

There is a growing interest in academia and industry for de signing and developing ambient multimedia environments. Numerous sensors, devices and multimedia services are avail able in such environment to support users in their lives. Surprisingly, users often find it difficult in interacting with such environments due to the presence of numerous devices and services. To address this issue, context-aware implicit or automatic interaction mechanisms have been proposed to facilitate easy access of the available devices and services and reduce the cognitive load of the user. However, such implicit interactions often lead to mis-automation due to imprecision in context information. This ultimately causes distrust and dissatisfaction to the user. This paper proposes a framework that considers quality of context information to dynamically adjust the level of implicit interaction and allows a system to operate in different modes ranging from full-automation to action suggestion to simple notification. Our initial user experiments demonstrate that dynamic and alternative mode of interaction not only increases the satisfaction of the users but also helps to avoid distrust in automated actions carried out by the ambient environment under varying contexts.

Data gathering in a timely and reliable fashion has been a key concern in Wireless Sensor Networks (WSNs) particularly related to military and surveillance applications. The introduction of real time communication has created additional challenges in this area with different communication constraints. Since sensor networks represent a new generation of time-critical applications, it is often necessary for communication to meet real time constraints. However, research dealing with providing QoS guarantees for real time traffic in sensor networks is still immature. Further, recently it has been shown that traffic arrival process in WSN does not follow the traditional Poisson model. Instead, it is found that an ON/OFF model is found to be most suitable to capture the bursty source traffic in WSN. To overcome the limitations in the area of performance evaluation of wireless sensor network, this paper proposes a novel analytical framework. The proposed analytical framework is based on a traffic model which is similar to an ON/OFF process, exhibits self-similar and long-range dependant characteristics. To provide performance guarantees to different applications in WSNs, the traffic model contemplates multiple classes of traffic. We analyze the model on the basis of G/M/l queuing system with a novel scheduling mechanism and find exact packet delay for corresponding traffic classes.

Current sensor-based monitoring systems use multiple sensors in order to identify high-level information based on the events that take place in the monitored environment. This information is obtained through low-level processing of sensory media streams, which are usually noisy and imprecise, leading to many undesired consequences such as false alarms, service interruptions, and often violation of privacy. Therefore, we need a mechanism to compute the quality of sensor-driven information that would help a user or a system in making an informed decision and improve the automated monitoring process. In this article, we propose a model to characterize such quality of information in a multisensor multimedia monitoring system in terms of certainty, accuracy/confidence and timeliness. Our model adopts a multimodal fusion approach to obtain the target information and dynamically compute these attributes based on the observations of the participating sensors. We consider the environment context, the agreement/disagreement among the sensors, and their prior confidence in the fusion process in determining the information of interest. The proposed method is demonstrated by developing and deploying a real-time monitoring system in a simulated smart environment. The effectiveness and suitability of the method has been demonstrated by dynamically assessing the value of the three quality attributes with respect to the detection and identification of human presence in the environment.

A multimedia surveillance system aims to provide security and safety of people in a monitored space. However, due to the nature of surveillance, privacy-sensitive information, such as face, gait and other physical parameters based on the captured media from multiple sensors, can be revealed without the concern of the people. This is a major concern in recent days. Therefore, it is desirable to have such mechanism that can hide privacy-sensitive information as much as possible, yet supporting effective surveillance tasks. In this paper, we propose a chaos cryptography based data hiding approach that can be applied on selected regions of interest (ROIs) in video camera footage, which contains privacy-sensitive data. Our approach also supports multiple levels of abstraction of data hiding depending on the role of the authorized user. In order to evaluate the suitability of this approach, we applied our algorithm on some video camera footage and observed that our approach is computationally efficient and applicable for real-time video surveillance tasks.

Interaction with the physical environment using mobile phones has become increasingly desirable and feasible. Nowadays mobile phones are being used to control different devices and access information/services related to those devices. To facilitate such interaction, devices are usually marked with RFID tags or visual markers, which are read by a mobile phone equipped with an integrated RFID reader or camera to fetch related information about those objects and initiate further actions. This article contributes in this domain of mobile physical interaction; however, using a spatial-geometric approach for interacting with indoor physical objects and artifacts instead of RFID based solutions. Using this approach, a mobile phone can point from a distance to an annotated object or a spatial subregion of that object for the purpose of interaction. The pointing direction and location is determined based on the fusion of IR camera and accelerometer data, where the IR cameras are used to calculate the 3D position of the mobile phone users and the accelerometer in the phone provides its tilting and orientation information. The annotation of objects and their subregions with which the mobile phone interacts is performed by specifying their geometric coordinates and associating related information or services with them. We perform experiment in a technology-augmented smart space and show the applicability and potential of the proposed approach.

Recently, there has been a high demand for developing tools that promote education through learning. We introduce our edutainment tool called Magic Stick that helps children learn about new objects by providing their names associated by visual representations regarding these objects. Children's parents or teachers can pick the entities they would like their children to learn about by simply attaching RFID tags to these entities. Afterwards, they can customize the type of information and visualizations related to these entities through the use of a friendly GUI designed for this purpose. In our study with young children, we found that the Magic Stick created an entertaining atmosphere among children and greatly engaged them in learning.

In this paper, we propose a motion-path based gesture recognition technique and show its application in a smart home environment. Users hand gestures are recognized by capturing the motion-path while they draw different symbols in the air. In order to capture the motion-path, we use infra-red camera's IR sensing capability. The IR camera tracks the infra-red emitter attached to the user's hand gloves and produces a sequence of motion-points, which are then analyzed syntactically to recognize the intended hand gesture. The recognized gesture is used to interact with the intelligent environment for accessing various services. Toggling a lamp switch, changing the light intensity, and playing/pausing a movie are few examples where we have integrated the gesture-based interaction. Our experiment shows that the proposed gesture recognition technique is robust and its use in the smart home environment is interesting and appealing to the people.

In large scale surveillance systems, a number of CCTV cameras are installed in distributed premises and are connected to a central control station, where human operators observe the different camera views for identifying a probable security breach. In such situations, it is particularly difficult for the operator to pay attention to all camera views. Studies have shown that a human operator can effectively monitor only four camera views at a time. This paper attempts to solve the problem of dynamically selecting and scheduling the four best CCTV views. We adopt a human-centric approach in which the system computes the operatorpsilas attention in the CCTV views to automatically determine the importance of events captured by the respective cameras. The experiments show that the proposed method helps a human operator in identifying important events occuring in the environment.

Multi-sensor systems are increasingly being deployed in many application scenarios due to the enormous potential they can offer. However, as the processing of sensory data often results in imprecise outcome, measuring the quality of information (QoI) in these systems has become an important issue. The measurement of QoI is usually performed by processing the elementary data provided by the heterogeneous sensors, which is also influenced by the techniques involved in sensor management. However, the effect of context, such as environmental geometry, sensor placement, orientation, time, and other parameters in computing QoI has not yet been explored extensively in the literature. This paper proposes a context evolution model and studies its impact in the QoI computation. In particular, we show that the dynamic context information can be utilized to manage a multi-sensor system to improve its QoI.

Metadata comes into forefront as a savior of multimedia search and management. However, the existence of the diverse set of metadata standards and the different vocabularies used by these standards has made that task especially challenging in recent days. In this paper, we propose a framework for managing multimedia content by effectively dealing with the heterogeneous multimedia metadata in a transparent fashion. The cornerstone of our approach is to leverage the existing metadata standards and provide schema and data level mapping among them. The proposed framework also allows adding new tags/vocabularies with the given metadata vocabularies, defining equivalency relationship among those vocabularies, and separately managing the metadata vocabularies outside of the actual media files. We have developed a prototype of the framework and evaluated its performance in terms of some basic query execution times

Navigation in 3D world has reached its pinnacle with the advent of several technologies like JAVA, XML, WEB SERVICES, VRML, X3D etc. A lot of efforts have been given to visualize and navigate in virtual mall, cities, digital libraries etc. Most of them visualize only static objects. We designed a Virtual Reality (VR) Tool, called Learning Object Repository Navigation and Authoring in Virtual environment (LORNAV) that extracts Learning Object Metadata (LOM) dynamically from repositories and creates 3D representation of these objects. The proposed tool provides several facilities such as personalized navigation allowing a user to view content that is of interest to him. It also helps users to create new aggregated Learning Objects (LOs) from existing ones in a 3D Environment to be presented in SMIL format.