Timothy Callemein

Timothy Callemein, Kristof Van Beeck, Toon Goedemé

In this paper, we present a room occupancy sensing solution with unique properties: (i) It is based on an omnidirectional vision camera, capturing rich scene info over a wide angle, enabling to count the number of people in a room and even their position. (ii) Although it uses a camera-input, no privacy issues arise because its extremely low image resolution, rendering people unrecognisable. (iii) The neural network inference is running entirely on a low-cost processing platform embedded in the sensor, reducing the privacy risk even further. (iv) Limited manual data annotation is needed, because of the self-training scheme we propose. Such a smart room occupancy rate sensor can be used in e.g. meeting rooms and flex-desks. Indeed, by encouraging flex-desking, the required office space can be reduced significantly. In some cases, however, a flex-desk that has been reserved remains unoccupied without an update in the reservation system. A similar problem occurs with meeting rooms, which are often under-occupied. By optimising the occupancy rate a huge reduction in costs can be achieved. Therefore, in this paper, we develop such system which determines the number of people present in office flex-desks and meeting rooms. Using an omnidirectional camera mounted in the ceiling, combined with a person detector, the company can intelligently update the reservation system based on the measured occupancy. Next to the optimisation and embedded implementation of such a self-training omnidirectional people detection algorithm, in this work we propose a novel approach that combines spatial and temporal image data, improving performance of our system on extreme low-resolution images.

Timothy Callemein, Kristof Van Beeck, Toon Goedemé

In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available. If these devices can be integrated in an embedded low-power sensor, it would form an ideal extension of automated room reservation systems in office environments. The main challenge we target here is ensuring the privacy of the people filmed. The approach we propose is going to extremely low image resolutions, such that it is impossible to recognise people or read potentially confidential documents. Therefore, we retrained a single-shot low-resolution person detection network with automatically generated ground truth. In this paper, we prove the functionality of this approach and explore how low we can go in resolution, to determine the optimal trade-off between recognition accuracy and privacy preservation. Because of the low resolution, the result is a lightweight network that can potentially be deployed on embedded hardware. Such embedded implementation enables the development of a decentralised smart camera which only outputs the required meta-data (i.e. the number of persons in the meeting room).

Timothy Callemein, Wiebe Van Ranst, Toon Goedemé

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.