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PointTrack++ for Effective Online Multi-Object Tracking and Segmentation | | Zhenbo Xu
; Wei Zhang
; Xiao Tan
; Wei Yang
; Xiangbo Su
; Yuchen Yuan
; Hongwu Zhang
; Shilei Wen
; Errui Ding
; Liusheng Huang
; | | Date: |
3 Jul 2020 | | Abstract: | Multiple-object tracking and segmentation (MOTS) is a novel computer vision
task that aims to jointly perform multiple object tracking (MOT) and instance
segmentation. In this work, we present PointTrack++, an effective on-line
framework for MOTS, which remarkably extends our recently proposed PointTrack
framework. To begin with, PointTrack adopts an efficient one-stage framework
for instance segmentation, and learns instance embeddings by converting compact
image representations to un-ordered 2D point cloud. Compared with PointTrack,
our proposed PointTrack++ offers three major improvements. Firstly, in the
instance segmentation stage, we adopt a semantic segmentation decoder trained
with focal loss to improve the instance selection quality. Secondly, to further
boost the segmentation performance, we propose a data augmentation strategy by
copy-and-paste instances into training images. Finally, we introduce a better
training strategy in the instance association stage to improve the
distinguishability of learned instance embeddings. The resulting framework
achieves the state-of-the-art performance on the 5th BMTT MOTChallenge. | | Source: | arXiv, 2007.1549 | | Services: | Forum | Review | PDF | Favorites | |
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