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Sweeney, Chris
STT: Stateful Tracking with Transformers for Autonomous Driving
Jing, Longlong, Yu, Ruichi, Chen, Xu, Zhao, Zhengli, Sheng, Shiwei, Graber, Colin, Chen, Qi, Li, Qinru, Wu, Shangxuan, Deng, Han, Lee, Sangjin, Sweeney, Chris, He, Qiurui, Hung, Wei-Chih, He, Tong, Zhou, Xingyi, Moussavi, Farshid, Guo, Zijian, Zhou, Yin, Tan, Mingxing, Yang, Weilong, Li, Congcong
Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset.
Aria Everyday Activities Dataset
Lv, Zhaoyang, Charron, Nicholas, Moulon, Pierre, Gamino, Alexander, Peng, Cheng, Sweeney, Chris, Miller, Edward, Tang, Huixuan, Meissner, Jeff, Dong, Jing, Somasundaram, Kiran, Pesqueira, Luis, Schwesinger, Mark, Parkhi, Omkar, Gu, Qiao, De Nardi, Renzo, Cheng, Shangyi, Saarinen, Steve, Baiyya, Vijay, Zou, Yuyang, Newcombe, Richard, Engel, Jakob Julian, Pan, Xiaqing, Ren, Carl
We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal open dataset recorded using Project Aria glasses. AEA contains 143 daily activity sequences recorded by multiple wearers in five geographically diverse indoor locations. Each of the recording contains multimodal sensor data recorded through the Project Aria glasses. In addition, AEA provides machine perception data including high frequency globally aligned 3D trajectories, scene point cloud, per-frame 3D eye gaze vector and time aligned speech transcription. In this paper, we demonstrate a few exemplar research applications enabled by this dataset, including neural scene reconstruction and prompted segmentation. AEA is an open source dataset that can be downloaded from https://www.projectaria.com/datasets/aea/. We are also providing open-source implementations and examples of how to use the dataset in Project Aria Tools https://github.com/facebookresearch/projectaria_tools.