Group detection represents an emerging Computer Vision research topic motivated by the increasing interest in modelling the social behaviour of people. This paper presents an unsupervised method for group detection which is based on an online inference process over Dirichlet Process Mixture Models. Formally, groups are modelled as components of an infinite mixture and individuals are seen as observations generated from them. The proposed sequential variational framework allows to perform inference in real-time, while social constraints based on proxemics rules ensure the production of proper group hypotheses consistent with human perception. The results obtained on several datasets compare favourably with state-of-the-art approaches, setting the best performance in some of them.

Abstract-We present a novel probabilistic framework that jointly models individuals and groups for tracking. Managing groups is challenging, primarily because of their nonlinear dynamics and complex layout which lead to repeated splitting and merging events. The proposed approach assumes a tight relation of mutual support between the modeling of individuals and groups, promoting the idea that groups are better modeled if individuals are considered and vice versa. This concept is translated in a mathematical model using a decentralized particle filtering framework which deals with a joint individual-group state space. The model factorizes the joint space into two dependent subspaces, where individuals and groups share the knowledge of the joint individual-group distribution. The assignment of people to the different groups (and thus group initialization, split and merge) is implemented by two alternative strategies: using classifiers trained beforehand on statistics of group configurations, and through online learning of a Dirichlet process mixture model, assuming that no training data is available before tracking. These strategies lead to two different methods that can be used on top of any person detector (simulated using the ground truth in our experiments). We provide convincing results on two recent challenging tracking benchmarks.

Sleep science is entering a new era, thanks to new data-driven analysis approaches that, combined with mouse gene–editing technologies, show a promise in functional genomics and translational research. However, the investigation of sleep is time consuming and not suitable for large-scale phenotypic datasets, mainly due to the need for subjective manual annotations of electrophysiological states. Moreover, the heterogeneous nature of sleep, with all its physiological aspects, is not fully accounted for by the current system of sleep stage classification. In this study, we present a new data-driven analysis approach offering a plethora of novel features for the characterization of sleep. This novel approach allowed for identifying several substages of sleep that were hidden to standard analysis. For each of these substages, we report an independent set of homeostatic responses following sleep deprivation. By using our new substages classification, we have identified novel differences among various genetic backgrounds. Moreover, in a specific experiment with the Zfhx3 mouse line, a recent circadian mutant expressing both shortening of the circadian period and abnormal sleep architecture, we identified specific sleep states that account for genotypic differences at specific times of the day. These results add a further level of interaction between circadian clock and sleep homeostasis and indicate that dissecting sleep in multiple states is physiologically relevant and can lead to the discovery of new links between sleep phenotypes and genetic determinants. Therefore, our approach has the potential to significantly enhance the understanding of sleep physiology through the study of single mutations. Moreover, this study paves the way to systematic high-throughput analyses of sleep.

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Large scale CMOS-MEAs are an emerging neurotechnology enabling extracellular recordings in-vitro and in-vivo with thousand's electrodes simultaneously. This is on the way to provide the unprecedented capability of acquiring signals from several thousands of single-units, thus opening novel perspectives for electrophysiology, but also novel challenges for analysis and management of large datasets. Here, we propose an analysis platform designed for managing unprecedentedly large datasets of electrical recordings acquired with a 4096-electrode array platform. Furthermore it provides a computational framework to facilitate the development and integration of new analysis tools exploiting high-resolution electrical recordings.