Features for Multi-Target Multi-Camera Tracking and Re-Identification
This work addresses tracking and re-identification challenges in multi-camera surveillance systems, representing an incremental improvement with novel training techniques.
The paper tackled the problem of learning features for Multi-Target Multi-Camera Tracking (MTMCT) and Person Re-Identification (Re-ID) using a convolutional neural network, achieving state-of-the-art results on benchmarks like DukeMTMC for tracking and Market-1501 and DukeMTMC-ReID for Re-ID.
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.