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Research map / Detection and segmentation / Tracking

TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model

CVPR 2020 · Detection and segmentation › Tracking · approach: Association cues for online MOT · 295 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use TubeTK without external detections instead of the strongest listed detection-based baselineMOT17 online tracking · MOT17MOTA: 60.0 -> 63.0Helped
Use TubeTK without external detections instead of TracktorMOT16 online tracking · MOT16MOTA: 54.5 -> 64.0Helped
Use TubeTK with POI detections instead of POIMOT16 tracking · MOT16MOTA: 66.1 -> 66.9Helped
Use TubeTK without external detections instead of RANMOT15 online tracking · MOT15MOTA: 56.5 -> 58.4Helped
Shorten input clips to l=4 (ablation)TubeTK · MOT17 trainMOTA: 76.9 -> 60.3Hurt
Fix all Btube lengths to 8 (ablation)TubeTK · MOT17 trainMOTA: 76.9 -> 74.3Hurt
Use dynamically selected Btube lengths rather than fixed lengths (ablation)TubeTK · MOT17 trainFN: 19452 -> 11801Helped
Replace Tube GIoU loss with Tube IoU loss (ablation)TubeTK · MOT17 trainMOTA: 76.9 -> 70.5Hurt
Replace Tube NMS with original NMS (ablation)TubeTK · MOT17 trainMOTA: 76.9 -> 75.3Hurt
Use Tube GIoU loss instead of Tube IoU loss (ablation)TubeTK · MOT17 trainFN: 18148 -> 11801Helped
Use Tube NMS instead of original NMS (ablation)TubeTK · MOT17 trainFN: 13421 -> 11801Helped

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