In recent years, life logging in daily environments has become increasingly important for applications such as health monitoring for older adults, behavior understanding, and optimization of daily activities. In particular, hand-object manipulation appears in a wide range of everyday situations,…
Learning driving policies that ensure safety is a fundamental problem in autonomous driving. A common approach is to learn decision-making policies for accident avoidance by leveraging both safe behavioral data (positive examples) and unsafe behavioral data (negative examples). However, in…
Forecasting urban dynamics—i.e., the spatial and temporal distribution of active populations in a city—is an important problem for applications such as store planning, congestion forecasting, and crowd management. Although many studies have addressed urban dynamics forecasting, providing early forecasts (e.g.,…
Forecasting the number of visitors for public events, which we call event crowd forecasting (ECF), has attracted much attention in recent years due to its practical importance. In particular, forecasting the event crowd can be used to predict the demand…
Imagine an event held in a city. For example, if the event is a sports game or a concert, the area around the venue will be crowded with visitors at the beginning and end of the event. Forecasting crowding at…
Currently, there is an increasing demand for technology that enables localization without possessing the devices such as smartphones. Up to now, device-free indoor localization has been performed using data such as video and thermal images, but there are still problems…
With the pandemic of COVID-19, indoor crowd density monitoring has become one of the most critical responsibilities of public space managers. The University of Tokyo released an indoor density monitoring system using BLE beacons to monitor the indoor crowd density.…
Forecasting the temporal transition of active population in a city, i.e., urban dynamics, is a very important topic from the viewpoint of urban planning, traffic control, and personnel allocation. We have been pursuing this topic in the vicinity of major…
In Wi-Fi fingerprint-based indoor localization, we need to acquire a dataset with a high-density dataset in the target environment in this framework. To overcome the data acquisition cost problem, we propose a brand new data augmentation for Wi-Fi indoor localization…
Driving behavior modeling (DBM) is widely used in the intelligent vehicle field to prevent accidents, which predicts actions that vehicles should take to optimize safe driving behaviors. According to some statistics, accidents easily happen at un-signalized intersections, modeling driving behavior…