A multi-modal person perception framework for socially interactive mobile service robots

In order to meet the increasing demands of mobile service robot applications, a dedicated perception module is an essential requirement for the interaction with users in real-world scenarios. In particular, multi sensor fusion and human re-identification are recognized as active research fronts. Through this paper we contribute to the topic and present a modular detection and tracking system that models position and additional properties of persons in the surroundings of a mobile robot. The proposed system introduces a probability-based data association method that besides the position can incorporate face and color-based appearance features in order to realize a re-identification of persons when tracking gets interrupted. The system combines the results of various state-of-the-art image-based detection systems for person recognition, person identification and attribute estimation. This allows a stable estimate of a mobile robot’s user, even in complex, cluttered environments with long-lasting occlusions. In our benchmark, we introduce a new measure for tracking consistency and show the improvements when face and appearance-based re-identification are combined. The tracking system was applied in a real world application with a mobile rehabilitation assistant robot in a public hospital. The estimated states of persons are used for the user-centered navigation behaviors, e.g., guiding or approaching a person, but also for realizing a socially acceptable navigation in public environments

Occupancy Detection using Wireless Sensor Network in Indoor Environment

Occupancy detection plays an important role in many smart buildings such as reducing building energy usage by controlling heating, ventilation and air conditioning (HVAC) systems, monitoring systems and managing lighting systems, tracking people in hospitals for medical issues, advertising to people in malls, and to search and rescue missions. The global positioning system (GPS) is used most widely as a localization system but highly inaccurate for indoor applications. The indoor environment is difficult to handle because along with the loss of signals, privacy is a major concern. Indoor tracking has many aspects in common with sensor localization in Wireless Sensor Networks (WSN). The contribution of this work is the demonstration of a nonintrusive approach to detect an occupancy in a building using wireless sensor networks to detect energy from cell phones in a secure facility and perform indoor localization based on the minimum mean square error (MMSE). To estimate the occupancy, the detected cellular signals information such as signal amplitude, frequency, power and detection time is sent to a fusion server, matched the detected signals by time and channel information, performed localization to estimate a location, and finally estimated the occupancy of rooms in a building from the estimated locations

Human worker activity recognition in industrial environments

In this work, an intelligent human-machine interface (HMI) for human worker activity recognition in industrial environments is presented. The interface consists of components for robust and accurate 3D position estimation in workspace environments, the recognition of task-related worker activities and human-computer interaction via gestures. All components of the presented HMI are flexible with respect to applications and can be transferred to other activity recognition problems, as well