TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera Vehicle Tracking
This is an incremental improvement for traffic monitoring systems, focusing on cross-camera vehicle tracking.
The paper tackles the problem of Multi-Target Multi-Camera vehicle tracking in traffic videos, addressing challenges like perspective variance and occlusions, and reports a competitive IDF1 score of 0.4733 on the AI City Challenge.
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.