  In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians'
accurate locations is crucial to traffic safety. Current systems adopt cameras
and wireless sensors to detect and estimate people's locations via sensor
fusion. Standard fusion algorithms, however, become inapplicable when
multi-modal data is not associated. For example, pedestrians are out of the
camera field of view, or data from camera modality is missing. To address this
challenge and produce more accurate location estimations for pedestrians, we
propose a Generative Adversarial Network (GAN) architecture. During training,
it learns the underlying linkage between pedestrians' camera-phone data
correspondences. During inference, it generates refined position estimations
based only on pedestrians' phone data that consists of GPS, IMU and FTM.
Results show that our GAN produces 3D coordinates at 1 to 2 meter localization
error across 5 different outdoor scenes. We further show that the proposed
model supports self-learning. The generated coordinates can be associated with
pedestrian's bounding box coordinates to obtain additional camera-phone data
correspondences. This allows automatic data collection during inference. After
fine-tuning on the expanded dataset, localization accuracy is improved by up to
26%.
