The autonomous hidden camera crew

Reality TV shows that follow people in their day-to-day lives are not a new concept. However, the traditional methods used in the industry require a lot of manual labour and need the presence of at least one physical camera man. Because of this, the subjects tend to behave differently when they are aware of being recorded. This paper will present an approach to follow people in their day-to-day lives, for long periods of time (months to years), while being as unobtrusive as possible. To do this, we use unmanned cinematographically-aware cameras hidden in people's houses. Our contribution in this paper is twofold: First, we create a system to limit the amount of recorded data by intelligently controlling a video switch matrix, in combination with a multi-channel recorder. Second, we create a virtual camera man by controlling a PTZ camera to automatically make cinematographically pleasing shots. Throughout this paper, we worked closely with a real camera crew. This enabled us to compare the results of our system to the work of trained professionals.Comment: 4 pages, 6 figure

A socio-economic approach to online vision graph generation and handover in distributed smart camera networks

Abstract—In this paper we propose an approach based on selfinterested autonomous cameras, which exchange responsibility for tracking objects in a market mechanism, in order to maximise their own utility. A novel ant-colony inspired mechanism is used to grow the vision graph during runtime, which may then be used to optimise communication between cameras. The key benefits of our completely decentralised approach are on the one hand generating the vision graph online which permits the addition and removal cameras to the network during runtime and on the other hand relying only on local information, increasing the robustness of the system. Since our market-based approach does not rely on a priori topology information, the need for any multicamera calibration can be avoided. Index Terms—Smart camera networks; multi-camera tracking; market-based control; topology identification; ant algorithms. I

Enhancing Activity Recognition by Fusing Inertial and Biometric Information

Activity recognition is an active research field nowadays, as it enables the development of highly adaptive applications, e.g. in the field of personal health. In this paper, a light high-level fusion algorithm to detect the activity that an individual is performing is presented. The algorithm relies on data gathered from accelerometers placed on different parts of the body, and on biometric sensors. Inertial sensors allow detecting activity by analyzing signal features such as amplitude or peaks. In addition, there is a relationship between the activity intensity and biometric response, which can be considered together with acceleration data to improve the accuracy of activity detection. The proposed algorithm is designed to work with minimum computational cost, being ready to run in a mobile device as part of a context-aware application. In order to enable different user scenarios, the algorithm offers best-effort activity estimation: its quality of estimation depends on the position and number of the available inertial sensors, and also on the presence of biometric information

The costs of fusion in smart camera networks

ABSTRACT The choice of the most suitable fusion scheme for smart camera networks depends on the application as well as on the available computational and communication resources. In this paper we discuss and compare the resource requirements of five fusion schemes, namely centralised fusion, flooding, consensus, token passing and dynamic clustering. The Extended Information Filter is applied to each fusion scheme to perform target tracking. Token passing and dynamic clustering involve negotiation among viewing nodes (cameras observing the same target) to decide which node should perform the fusion process whereas flooding and consensus do not include this negotiation. Negotiation helps limiting the number of participating cameras and reduces the required resources for the fusion process itself but requires additional communication. Consensus has the highest communication and computation costs but it is the only scheme that can be applied when not all viewing nodes are connected directly and routing tables are not available

A Stochastic Resampling Based Selective Particle Filter for Robust Visual Object Tracking

In this work, a new variant of particle filter has been proposed. In visual object tracking, particle filters have been used popularly because they are compatible with system non-linearity and non-Gaussian posterior distribution. But the main problem in particle filtering is sample degeneracy. To solve this problem, a new variant of particle filter has been proposed. The resampling algorithm used in this proposed particle filter is derived by combining systematic resampling, which is commonly used in SIR-PF (Sampling Importance Resampling Particle Filter) and a modified bat algorithm; this resampling algorithm reduces sample degeneracy as well as sample impoverishments. The measurement model is modified to handle clutter in presence of varying background. A new motion dynamics model is proposed which further reduces the chance of sample degeneracy among the particles by adaptively shifting mean of the process noise. To deal with illumination fluctuation and object deformation in presence of complete occlusion, a template update algorithm has also been proposed. This template update algorithm can update template even when the difference in the spread of the color-histogram is especially large over time. The proposed tracker has been tested against many challenging conditions and found to be robust against clutter, illumination change, scale change, fast object movement, motion blur, and complete occlusion; it has been found that the proposed algorithm outperforms the SIR-PF (Sampling Importance Resampling Particle Filter), bat algorithm and some other state-of-the-art tracking algorithms