Cross-View Person Identification by Matching Human Poses Estimated With Confidence on Each Body Joint

Abstract

Cross-view person identification (CVPI) from multiple temporally synchronized videos taken by multiple wearable cameras from different, varying views is a very challenging but important problem, which has attracted more interests recently. Current state-of-the-art performance of CVPI is achieved by matching appearance and motion features across videos, while the matching of pose features does not work effectively given the high inaccuracy of the 3D human pose estimation on videos/images collected in the wild. In this paper, we introduce a new metric of confidence to the 3D human pose estimation and show that the combination of the inaccurately estimated human pose and the inferred confidence metric can be used to boost the CVPI performance---the estimated pose information can be integrated to the appearance and motion features to achieve the new state-of-the-art CVPI performance. More specifically, the estimated confidence metric is measured at each human-body joint and the joints with higher confidence are weighted more in the pose matching for CVPI. In the experiments, we validate the proposed method on three wearable-camera video datasets and compare the performance against several other existing CVPI methods.