TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera
Vehicle Tracking
- URL: http://arxiv.org/abs/2205.13857v1
- Date: Fri, 27 May 2022 09:40:00 GMT
- Title: TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera
Vehicle Tracking
- Authors: David Serrano, Francesc Net, Juan Antonio Rodr\'iguez and Igor Ugarte
- Abstract summary: We present TrackNet, a method for Multi-Target Multi-Camera (MTMC) vehicle tracking from traffic video sequences.
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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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