Abstract
Multi-Target Multi-Camera Tracking (MTMCT) tracks
many people through video taken from several cameras.
Person Re-Identification (Re-ID) retrieves from a gallery
images of people similar to a person query image. We
learn good features for both MTMCT and Re-ID with a convolutional neural network. Our contributions include an
adaptive weighted triplet loss for training and a new technique for hard-identity mining. Our method outperforms
the state of the art both on the DukeMTMC benchmarks for
tracking, and on the Market-1501 and DukeMTMC-ReID
benchmarks for Re-ID. We examine the correlation between
good Re-ID and good MTMCT scores, and perform ablation studies to elucidate the contributions of the main components of our system. Code is available