Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories

Abstract

Detecting pedestrians moving together through public spaces can provide relevant information for many location-based social applications. In this work we present an online method to detect such pedestrian flocks by spatio-temporal clustering of location trajectories. Compared to prior work, our method provides increased robustness against the influence of noisy and missing GPS data often encountered in urban environments. To assess the performance of the method, we record GPS trajectories from ten subjects walking through a city. The data set contains various flock formations and corresponding ground truth information is available. With this data set, we can evaluate the accuracy of our method to detect flocks. Results show that we can detect flocks and their members with an accuracy of 91.3%. We evaluate the influence of noisy and missing location data on the detection accuracy and show that the introduced filtering heuristics provides increased detection accuracy in such realistic situations.