Pedestrian Tracking in Public Passageway by Single 3D Depth Sensor
Riki Ukyo, Tatsuya Amano, Akihito Hiromori, Hirozumi Yamaguchi · 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops) · 2022
We propose an approach to pedestrian tracking in a public passageway with various pedestrians, such as those carrying luggage and baby strollers and family groups close to each other, based on 3D point cloud data captured by a single 3D depth sensor. Since we assume a wall-attached sensor, which is easy to deploy in passageways, pedestrians walking nearby the sensor frequently occlude the others behind. This causes a severe error in pedestrian segmentation in the 3D point cloud and Kalman-filter-based tracking. We introduce a new technique to spatially complement the missing part of segments in KF-based multi-object tracking to cope with this issue. We have evaluated our method using the 3D point cloud data capturing pedestrians at the entrance of an existing commercial facility (shopping small), as well as the one collected in our laboratory space. As a result, the tracking accuracy index (MOTA) for multiple objects is 0.914, with severe and frequent occlusions.