  In this technical report, we describe our EfficientLPT architecture that won
the panoptic tracking challenge in the 7th AI Driving Olympics at NeurIPS 2021.
Our architecture builds upon the top-down EfficientLPS panoptic segmentation
approach. EfficientLPT consists of a shared backbone with a modified
EfficientNet-B5 model comprising the proximity convolution module as the
encoder followed by the range-aware FPN to aggregate semantically rich
range-aware multi-scale features. Subsequently, we employ two task-specific
heads, the scale-invariant semantic head and hybrid task cascade with feedback
from the semantic head as the instance head. Further, we employ a novel
panoptic fusion module to adaptively fuse logits from each of the heads to
yield the panoptic tracking output. Our approach exploits three consecutive
accumulated scans to predict locally consistent panoptic tracking IDs and also
the overlap between the scans to predict globally consistent panoptic tracking
IDs for a given sequence. The benchmarking results from the 7th AI Driving
Olympics at NeurIPS 2021 show that our model is ranked #1 for the panoptic
tracking task on the Panoptic nuScenes dataset.
