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Amodal perception, the ability to comprehend complete object structures frompartial visibility, is a fundamental skill, even for infants. Its significanceextends to applications like autonomous driving, where a clear understanding ofheavily occluded objects is essential. However, modern detection and trackingalgorithms often overlook this critical capability, perhaps due to theprevalence of \textit{modal} annotations in most benchmarks. To address thescarcity of amodal benchmarks, we introduce TAO-Amodal, featuring 833 diversecategories in thousands of video sequences. Our dataset includes\textit{amodal} and modal bounding boxes for visible and partially or fullyoccluded objects, including those that are partially out of the camera frame.We investigate the current lay of the land in both amodal tracking anddetection by benchmarking state-of-the-art modal trackers and amodalsegmentation methods. We find that existing methods, even when adapted foramodal tracking, struggle to detect and track objects under heavy occlusion. Tomitigate this, we explore simple finetuning schemes that can increase theamodal tracking and detection metrics of occluded objects by 2.1\% and 3.3\%.

TAO-Amodal: A Benchmark for Tracking Any Object Amodally