Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project
This work addresses multi-camera tracking for surveillance or security applications, but it is incremental as it uses existing methods on a known dataset.
The authors tackled multi-camera tracking, a challenging problem with less attention than single-camera tracking, by applying simple hierarchical clustering with well-trained person re-identification features on the DukeMTMC benchmark, achieving good results.
Although many methods perform well in single camera tracking, multi-camera tracking remains a challenging problem with less attention. DukeMTMC is a large-scale, well-annotated multi-camera tracking benchmark which makes great progress in this field. This report is dedicated to briefly introduce our method on DukeMTMC and show that simple hierarchical clustering with well-trained person re-identification features can get good results on this dataset.