Geert Heijenk

Fatemeh Marzani, Thijs van Ede, Geert Heijenk et al.

An important aspect of crowd monitoring is knowing how many people we are dealing with. Sometimes, knowing the size of a crowd in a single location and at a specific moment is enough. Matters become problematic when counting the same people across dif ferent locations or counting them over longer periods of time. In those cases, we need to identify and later reidentify a person, which immediately leads to privacy concerns. Until recently, solutions have been based on unique identification of carry-on devices, yet privacy improvements have caused transmitted information to be randomized, rendering this technique mostly useless. We propose to use biometric data instead. We introduce a pipeline that counts people based on face recognition, yet without ever being able to reveal the identity of individuals. To count, a camera initially detects a face, extracts its features, and derives an identifier using a fuzzy extractor. The original facial image is then deleted. Identifiers are inserted into homomorphically encrypted Bloom filters. This allows oblivious set membership testing directly on encrypted data, enabling the system to count across locations or across different moments, without revealing any identities. We provide an initial evaluation of our method that shows promising results.

Francesco Acciani, Paolo Frasca, Geert Heijenk et al.

This paper is about obtaining stable vehicle platooning by using Cooperative Adaptive Cruise Control when the communication is unreliable and suffers from message losses. We model communication losses as independent random events and we propose an original design for the cooperative controller, which mitigates the effect of losses. This objective is obtained by a switching controller that has a twofold objective: on the one hand, it promotes both plant stability and string stability of the average error dynamics by an $H_infty$ approach, and on the other hand it minimizes the variance around the average. We show by simulations that the proposed controller is able to compensate even for high probability of losses.

Mitra Baratchi, Geert Heijenk, Maarten van Steen

In this paper, we address the problem of how automated situation-awareness can be achieved by learning real-world situations from ubiquitously generated mobility data. Without semantic input about the time and space where situations take place, this turns out to be a fundamental challenging problem. Uncertainties also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for finding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the category, and the actual identity of a space can be discovered from the extracted situation patterns. Doing so, we use different real-world mobility datasets with data about the presence of mobile entities in a variety of spaces. We also evaluate the performance of this technique by showing its robustness against uncertainties.