TAO-Amodal dataset features diverse (880 categories) annotations for both
Traditional tracking (top) and Amodal tracking (bottom).
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%.
Traditional perception (top) concentrates on identifying visible segments. Consequently, they face peculiar outputs such as vanishing bounding boxes or tiny box sizes under occlusion scenarios. Amodal perception (bottom) advances beyond conventional approaches by inferring complete object boundaries, even when certain portions are occluded.
Our dataset augments the TAO dataset with amodal bounding box annotations for fully invisible, out-of-frame, and occluded objects across 880 categories. Note that this implies TAO-Amodal also includes modal segmentation masks.
Our Amodal Expander serves as a plug-in module that can ``amodalize" any existing detector or tracker with limited (amodal) training data. Here we provide qualitative results of both modal (top) and amodal (bottom) predictions from amodal expander.
@article{hsieh2023tracking,
title={Tracking any object amodally},
author={Hsieh, Cheng-Yen and Khurana, Tarasha and Dave, Achal and Ramanan, Deva},
journal={arXiv preprint arXiv:2312.12433},
year={2023}
}