Spatio-temporal representation for long-term anticipation of human presence in service robotics

We propose an efficient spatio-temporal model for mobile autonomous robots operating in human populated environments. Our method aims to model periodic temporal patterns of people presence, which are based on peoples’ routines and habits. The core idea is to project the time onto a set of wrapped dimensions that represent the periodicities of people presence. Extending a 2D spatial model with this multi-dimensional representation of time results in a memory efficient spatio-temporal model. This model is capable of long-term predictions of human presence, allowing mobile robots to schedule their services better and to plan their paths. The experimental evaluation, performed over datasets gathered by a robot over a period of several weeks, indicates that the proposed method achieves more accurate predictions than the previous state of the art used in robotics

Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

This work presents a non-parametric spatiotemporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Survey on video anomaly detection in dynamic scenes with moving cameras

The increasing popularity of compact and inexpensive cameras, e.g.~dash cameras, body cameras, and cameras equipped on robots, has sparked a growing interest in detecting anomalies within dynamic scenes recorded by moving cameras. However, existing reviews primarily concentrate on Video Anomaly Detection (VAD) methods assuming static cameras. The VAD literature with moving cameras remains fragmented, lacking comprehensive reviews to date. To address this gap, we endeavor to present the first comprehensive survey on Moving Camera Video Anomaly Detection (MC-VAD). We delve into the research papers related to MC-VAD, critically assessing their limitations and highlighting associated challenges. Our exploration encompasses three application domains: security, urban transportation, and marine environments, which in turn cover six specific tasks. We compile an extensive list of 25 publicly-available datasets spanning four distinct environments: underwater, water surface, ground, and aerial. We summarize the types of anomalies these datasets correspond to or contain, and present five main categories of approaches for detecting such anomalies. Lastly, we identify future research directions and discuss novel contributions that could advance the field of MC-VAD. With this survey, we aim to offer a valuable reference for researchers and practitioners striving to develop and advance state-of-the-art MC-VAD methods.Comment: Under revie

Inferring Temporal Models of People Presence from Environment Structrure

Cílem této práce je prezentovat projekt FreMEn contra COVID, jeho technickou stránku, a experimentálně ohodnotit jeho přínos. Aby bylo možné systém nasadit v nových oblastech i přes malé množství dat, možnost přenosu chronorobotických temporálních modelů, při modelovaní lidského davového chování, je taktéž testována. Přenos temporálních modelů je způsob, jak se vypořádat s extrémně malými množství dat, běžnými pro robotiku, která jsou velkým problémem při potřebě rychlého nasazení robotického systému, pokud je jeho funkcionalita na temporálních modelech závislá. Z důvodu společenské potřeby, způsobené světovou pandemií v roce 2020⁠—kvůli které project FreMEn contra COVID vznikl⁠—přenos temporálních modelů byl vyhodnocen pro zlepšení aplikace Nebojsa (angl. FreMEn Advisor) s cílem pomoci lidem zavést do jejich života principy sociálního odstupu. Tato aplikace doporučuje čas k návštěvě veřejných míst, kde jsou vysoké koncentrace lidí tak, aby se mohl uživatel vyhnout vytíženým časům. Společně s přenosem temporálních modelů i efekt systému Nebojsa na riziko, kterému se lidé vystavují při nutných pochůzkách, je testován včetně různých možných zdrojů predikcí zaplněnosti daných míst. Výsledky ukazují, že riziko je významně nižší pro uživatele, kteří se řídí doporučeními a že přenesené modely jsou slibným způsobem, jak službu zajistit i v místech, která nejsou pokrata daty pro přesné modelování.The goal of this thesis is to present the FreMEn contra COVID project from a technical perspective and experimentally evaluate its impact. To allow for deployment in new areas, because of low amounts of data the possibility of transferring chronorobotic temporal models in the application is tested. Temporal transfer presents a way to deal with extremely small amounts of data, common to robotics, that pose a significant problem to quick deployment of robotic systems dependent on those. Because of the social need caused by the world-wide pandemic of 2020⁠—for which the FreMEn contra COVID project was founded⁠—the temporal transfer has been evaluated in the context of boosting the performance of a system meant to aid individuals to implement social distancing measures FreMEn Advisor app. This app gives recommendations to the time of visits to public locations, where many people concentrate so that the user can avoid crowded times. With the temporal transfer, also the impact of the FreMEn Advisor is tested to the risk people experience while doing necessary tasks in public places, like shops, with different possible sources of predictions of human behaviour in given places. The results show that the risk is significantly lower for users following the recommendations and that transferred models present a promising way to provide recommendations for places not covered by data for exact modelling

Time-varying Pedestrian Flow Models for Service Robots

We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples’ routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling