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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