第7回AIドライビングオリンピック：パノプティックトラッキング優勝報告
7th AI Driving Olympics: 1st Place Report for Panoptic Tracking
NeurIPS 2021の第7回AIドライビングオリンピックのパノプティックトラッキング部門で1位を獲得したEfficientLPTアーキテクチャを提案する。連続スキャンを活用して局所的・大域的に一貫したトラッキングIDを予測する。
著者: Rohit Mohan, Abhinav Valada
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
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.
