TAO-Amodal: A Benchmark for Tracking Any Object Amodally
arxiv(2023)
摘要
Amodal perception, the ability to comprehend complete object structures from
partial visibility, is a fundamental skill, even for infants. Its significance
extends to applications like autonomous driving, where a clear understanding of
heavily occluded objects is essential. However, modern detection and tracking
algorithms often overlook this critical capability, perhaps due to the
prevalence of modal annotations in most benchmarks. To address the
scarcity of amodal benchmarks, we introduce TAO-Amodal, featuring 833 diverse
categories in thousands of video sequences. Our dataset includes
amodal and modal bounding boxes for visible and partially or fully
occluded objects, including those that are partially out of the camera frame.
We investigate the current lay of the land in both amodal tracking and
detection by benchmarking state-of-the-art modal trackers and amodal
segmentation methods. We find that existing methods, even when adapted for
amodal tracking, struggle to detect and track objects under heavy occlusion. To
mitigate this, we explore simple finetuning schemes that can increase the
amodal tracking and detection metrics of occluded objects by 2.1% and 3.3%.
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