Person segmentation and identification across multiple wearable cameras

Recent major developments in the understanding of human social interactions have greatly contributed to the development of computers with social interaction capabilities. Many studies have investigated the understanding of human interaction from cameras. Identification of people across multiple videos is important for exploring human social interactions in group activities. We propose a framework for person segmentation and identification across videos captured by multiple wearable cameras. The proposed method comprises a local tracking module for tracking people in a single video and a global matching module for matching people across multiple videos. The method uses global consistency to identify people across multiple videos as well as ensures spatial-temporal consistency in a single video. We have demonstrated the effectiveness of our proposed method in comparison with a baseline method by using public datasets and our own dataset.