  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.
