| Title: |
TAO: A Large-Scale Benchmark for Tracking Any Object |
| Authors: |
Dave, Achal; Khurana, Tarasha; Tokmakov, Pavel; Schmid, Cordelia; Ramanan, Deva |
| Contributors: |
Carnegie Mellon University Pittsburgh (CMU); Models of visual object recognition and scene understanding (WILLOW); Département d'informatique - ENS-PSL (DI-ENS); École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL); Université Paris Sciences et Lettres (PSL)-Université Paris Sciences et Lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria de Paris; Institut National de Recherche en Informatique et en Automatique (Inria); Apprentissage de modèles à partir de données massives (Thoth); Centre Inria de l'Université Grenoble Alpes; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jean Kuntzmann (LJK); Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP); Université Grenoble Alpes (UGA); Argo AI; This work was supported in part by the CMU Argo AI Center for Autonomous Vehicle Research, the Inria associate team GAYA, and by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DOI/IBC) contract number D17PC00345. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied of IARPA, DOI/IBC or the U.S. Government. This work was funded in part by the French government under the management of Agence Nationale de la Recherche as part of the “Investissements davenir” program, reference ANR-19-P3IA-0001 (PRAIRIE3IA Institute).; Inria_CMU_GAYA; ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019) |
| Source: |
ECCV 2020 - European Conference on Computer Vision ; https://hal.science/hal-02951747 ; ECCV 2020 - European Conference on Computer Vision, Aug 2020, Glasgow / Virtual, United Kingdom. pp.436-454, ⟨10.1007/978-3-030-58558-7_26⟩ |
| Publisher Information: |
CCSD; Springer |
| Publication Year: |
2020 |
| Collection: |
Université Grenoble Alpes: HAL |
| Subject Terms: |
Tracking; Video object detection; Datasets; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [STAT.OT]Statistics [stat]/Other Statistics [stat.ML] |
| Subject Geographic: |
Glasgow / Virtual; United Kingdom |
| Description: |
International audience ; For many years, multi-object tracking benchmarks have focused on a handful of categories. Motivated primarily by surveillance and self-driving applications, these datasets provide tracks for people, vehicles, and animals, ignoring the vast majority of objects in the world. By contrast, in the related field of object detection, the introduction of large-scale, diverse datasets (e.g., COCO) have fostered significant progress in developing highly robust solutions. To bridge this gap, we introduce a similarly diverse dataset for Tracking Any Object (TAO) 4. It consists of 2,907 high resolution videos, captured in diverse environments, which are half a minute long on average. Importantly, we adopt a bottom-up approach for discovering a large vocabulary of 833 categories, an order of magnitude more than prior tracking benchmarks. To this end, we ask annotators to label objects that move at any point in the video, and give names to them post factum. Our vocabulary is both significantly larger and qualitatively different from existing tracking datasets. To ensure scalability of annotation, we employ a federated approach that focuses manual effort on labeling tracks for those relevant objects in a video (e.g., those that move). We perform an extensive evaluation of state-of-the-art trackers and make a number of important discoveries regarding large-vocabulary tracking in an open-world. In particular, we show that existing single-and multi-object trackers struggle when applied to this scenario in the wild, and that detection-based, multi-object trackers are in fact competitive with user-initialized ones. We hope that our dataset and analysis will boost further progress in the tracking community. |
| Document Type: |
conference object |
| Language: |
English |
| DOI: |
10.1007/978-3-030-58558-7_26 |
| Availability: |
https://hal.science/hal-02951747; https://hal.science/hal-02951747v1/document; https://hal.science/hal-02951747v1/file/tao_dataset.pdf; https://doi.org/10.1007/978-3-030-58558-7_26 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.72B86F7 |
| Database: |
BASE |