TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera Vehicle Tracking
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 27 May 2022.
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References · 9
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2017Mask R-CNNcited by 7,259
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
- 2017Simple Online and Realtime Tracking with a Deep Association Metriccited by 908
- 2016Simple Online and Realtime Trackingcited by 862
- 2017Focal Loss for Dense Object Detectioncited by 749
- 2019CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identificationcited by 119
- 2018Features for Multi-Target Multi-Camera Tracking and Re-Identificationcited by 88
- 2021The 5th AI City Challengecited by 28
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