D^3: Duplicate Detection Decontaminator for Multi-Athlete Tracking in Sports Videos

by   Rui He, et al.

Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, since athletes often have the same appearance and are intimately covered with each other, making a common occlusion problem becomes an abhorrent duplicate detection. In this paper, the duplicate detection is newly and precisely defined as occlusion misreporting on the same athlete by multiple detection boxes in one frame. To address this problem, we meticulously design a novel transformer-based Duplicate Detection Decontaminator (D^3) for training, and a specific algorithm Rally-Hungarian (RH) for matching. Once duplicate detection occurs, D^3 immediately modifies the procedure by generating enhanced boxes losses. RH, triggered by the team sports substitution rules, is exceedingly suitable for sports videos. Moreover, to complement the tracking dataset that without shot changes, we release a new dataset based on sports video named RallyTrack. Extensive experiments on RallyTrack show that combining D^3 and RH can dramatically improve the tracking performance with 9.2 in MOTA and 4.5 in HOTA. Meanwhile, experiments on MOT-series and DanceTrack discover that D^3 can accelerate convergence during training, especially save up to 80 percent of the original training time on MOT17. Finally, our model, which is trained only with volleyball videos, can be applied directly to basketball and soccer videos for MAT, which shows priority of our method. Our dataset is available at https://github.com/heruihr/rallytrack.


page 2

page 6

page 9

page 14


Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark

Satellite video cameras can provide continuous observation for a large-s...

Unified Transformer Tracker for Object Tracking

As an important area in computer vision, object tracking has formed two ...

Multi-Class Multi-Object Tracking using Changing Point Detection

This paper presents a robust multi-class multi-object tracking (MCMOT) f...

Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections

In currently available literature, no tracking-by-detection (TBD) paradi...

OmniTracker: Unifying Object Tracking by Tracking-with-Detection

Object tracking (OT) aims to estimate the positions of target objects in...

DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks

In this paper, we study masked autoencoder (MAE) pretraining on videos f...

Understanding 3D Object Articulation in Internet Videos

We propose to investigate detecting and characterizing the 3D planar art...

Please sign up or login with your details

Forgot password? Click here to reset