Do Different Tracking Tasks Require Different Appearance Models?
Tracking objects of interest in a video is one of the most popular and widely applicable problems in computer vision. However, with the years, a Cambrian explosion of use cases and benchmarks has fragmented the problem in a multitude of different experimental setups. As a consequence, the literature has fragmented too, and now novel approaches proposed by the community are usually specialised to fit only one specific setup. To understand to what extent this specialisation is necessary, in this work we present UniTrack, a solution to address five different tasks within the same framework. UniTrack consists of a single and task-agnostic appearance model, which can be learned in a supervised or self-supervised fashion, and multiple ``heads'' that address individual tasks and do not require training. We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered. The framework also allows us to analyse appearance models obtained with the most recent self-supervised methods, thus extending their evaluation and comparison to a larger variety of important problems.
PDF Abstract NeurIPS 2021 PDF NeurIPS 2021 AbstractCode
Tasks
Multi-Object Tracking
Multi-Object Tracking and Segmentation
Multiple People Tracking
Object Tracking
Online Multi-Object Tracking
Pose Estimation
Pose Prediction
Pose Tracking
Semi-Supervised Video Object Segmentation
Video Instance Segmentation
Video Object Segmentation
Video Object Tracking
Visual Object Tracking
Visual Tracking
ImageNet
DAVIS
OTB
DAVIS 2017
JHMDB
MOTChallenge
OTB-2015
YouTube-VIS 2019
MOT16
PoseTrack
Results from the Paper
Ranked #2 on
Video Object Segmentation
on DAVIS 2017
(mIoU metric)
