MOPT: Multi-Object Panoptic Tracking
Juana Valeria Hurtado, Rohit Mohan, Wolfram Burgard, Abhinav Valada

TL;DR
This paper introduces multi-object panoptic tracking (MOPT), a unified perception task combining segmentation and tracking to enhance scene understanding for autonomous robots, supported by a new architecture and evaluation metric.
Contribution
It proposes the novel MOPT task, a unified framework for segmentation and tracking, along with the PanopticTrackNet architecture and the sPTQ metric for comprehensive evaluation.
Findings
Encouraging results on vision-based MOPT
Effective integration of segmentation and tracking tasks
Strong baseline performances established
Abstract
Comprehensive understanding of dynamic scenes is a critical prerequisite for intelligent robots to autonomously operate in their environment. Research in this domain, which encompasses diverse perception problems, has primarily been focused on addressing specific tasks individually rather than modeling the ability to understand dynamic scenes holistically. In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking. MOPT allows for exploiting pixel-level semantic information of 'thing' and 'stuff' classes, temporal coherence, and pixel-level associations over time, for the mutual benefit of each of the individual sub-problems. To facilitate quantitative evaluations of MOPT in a unified manner, we propose the soft panoptic…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Advanced Neural Network Applications
