  We present TrackNet, a method for Multi-Target Multi-Camera (MTMC) vehicle
tracking from traffic video sequences. Cross-camera vehicle tracking has proved
to be a challenging task due to perspective, scale and speed variance, as well
occlusions and noise conditions. Our method is based on a modular approach that
first detects vehicles frame-by-frame using Faster R-CNN, then tracks
detections through single camera using Kalman filter, and finally matches
tracks by a triplet metric learning strategy. We conduct experiments on
TrackNet within the AI City Challenge framework, and present competitive IDF1
results of 0.4733.
