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

Detection and tracking of a human on a bicycle using HOG feature and particle filter

Detection of a human on a bicycle is an important research subject in an advanced safety vehicle driving system to decrease traffic accidents. The Histograms of Oriented Gradients (HOG) feature has been proposed as useful feature for detecting a standing human in various kinds of background. So, many researchers use currently the HOG feature to detect a human. Detecting a human on a bicycle is more difficult than detecting a standing human, because the appearance of a bicycle can change dramatically according to viewpoints. In this paper, we propose a method of detecting a human on a bicycle using HOG feature and RealAdaBoost algorithm. When detecting a human on a bicycle, occlusion is a cause of decreasing detection efficiency. Occlusion is a serious problem in car vision research, because there are often occlusion in real transportation environment. In such a case, the proposed method predicts the next position of a human on a bicycle using a tracking strategy. Experimental results and their evaluation show satisfactory performance of the proposed method