2,089 research outputs found
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion
A typical pipeline for multi-object tracking (MOT) is to use a detector for
object localization, and following re-identification (re-ID) for object
association. This pipeline is partially motivated by recent progress in both
object detection and re-ID, and partially motivated by biases in existing
tracking datasets, where most objects tend to have distinguishing appearance
and re-ID models are sufficient for establishing associations. In response to
such bias, we would like to re-emphasize that methods for multi-object tracking
should also work when object appearance is not sufficiently discriminative. To
this end, we propose a large-scale dataset for multi-human tracking, where
humans have similar appearance, diverse motion and extreme articulation. As the
dataset contains mostly group dancing videos, we name it "DanceTrack". We
expect DanceTrack to provide a better platform to develop more MOT algorithms
that rely less on visual discrimination and depend more on motion analysis. We
benchmark several state-of-the-art trackers on our dataset and observe a
significant performance drop on DanceTrack when compared against existing
benchmarks. The dataset, project code and competition server are released at:
\url{https://github.com/DanceTrack}.Comment: add change lo
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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