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RI:Small: Improve Visual Tracking by Large Scale Learning, Diagnosis, and Evaluation

RI:Small：通过大规模学习、诊断和评估改进视觉跟踪

基本信息

批准号：
2006665
负责人：
Haibin Ling
金额：
$ 49.83万
依托单位：
SUNY at Stony Brook
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2006665&HistoricalAwards=false
关键词：
RI Small Improve Visual Tracking

项目摘要

Video understanding and analysis has a wide range of applications. As a cornerstone in video understanding, visual tracking provides online motion information of objects of interests, such as walking pedestrians in autonomous driving, moving cells in bioengineering study, and deforming guidewire in medical intervention, to name a few. Despite recent advances in deep learning-based visual tracking systems, however, a significant gap remains between state-of-the-art algorithms and real-world applications. A conjecture is that the advantage of deep learning is not fully explored, especially due to the lack of large-scale quality tracking datasets. Currently, the largest published fully annotated tracking dataset contains less than 2,000 videos, which are hardly sufficient for effectively learning a robust tracking model. This project confronts the issue by directly and explicitly working on large-scale learning of tracking algorithms, and aims to improving tracker systems from various aspects including accuracy, efficiency, robustness, as well as generalization capability. The produced datasets, benchmark, diagnosis toolkit, tracking algorithms and temporal modeling techniques, will be made publicly available and expected to generate significant contributions to the computer vision and related fields.The overall goal of this research is to push the frontier of visual object tracking though large-scale learning. The project divides the research activities into three thrusts. Firstly, large-scale quality tracking datasets will be constructed with full annotation. Based on such datasets, an online benchmark platform will be derived and a tracking diagnosis toolkit be developed for studying challenge factors in visual tracking. These results will provide the data basis, test beds, and analytic tools for facilitating research in visual tracking. Secondly, efforts will be devoted to improving the robustness of deep trackers against various challenge factors, by either optimizing tracker architectures or integrating predictions of these factors. Thirdly, effective deep temporal models will be developed in two ways: one implicitly encodes temporal information in joint spatial-temporal CNN structures, while the other develops attention-guided dual memory models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

视频理解和分析有着广泛的应用。作为视频理解的基石，视觉跟踪提供感兴趣对象的在线运动信息，例如自动驾驶中的步行行人，生物工程研究中的移动细胞，以及医疗干预中的变形导丝等。尽管最近在基于深度学习的视觉跟踪系统方面取得了进展，但是，最先进的算法和现实世界的应用之间仍然存在着巨大的差距。一个猜想是，深度学习的优势尚未得到充分挖掘，特别是由于缺乏大规模的高质量跟踪数据集。目前，最大的已发布的完全注释的跟踪数据集包含不到2,000个视频，这几乎不足以有效地学习鲁棒的跟踪模型。该项目通过直接和明确地研究跟踪算法的大规模学习来面对这个问题，旨在从准确性，效率，鲁棒性和泛化能力等各个方面改进跟踪器系统。本研究的目标是通过大规模学习来推动视觉目标跟踪的前沿研究，并将所产生的数据集、基准测试、诊断工具、跟踪算法和时间建模技术公开发布，以期为计算机视觉及相关领域做出重大贡献。该项目将研究活动分为三个重点。首先，将构建具有完整注释的大规模质量跟踪数据集。基于这些数据集，将衍生出一个在线基准平台，并开发一个跟踪诊断工具包，用于研究视觉跟踪中的挑战因素。这些结果将提供数据基础，测试床和分析工具，促进视觉跟踪的研究。其次，将致力于通过优化跟踪器架构或整合这些因素的预测来提高深度跟踪器对各种挑战因素的鲁棒性。第三，有效的深度时间模型将以两种方式开发：一种是在联合时空CNN结构中隐式编码时间信息，另一种是开发注意力引导的双重记忆模型。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（17）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

GTCaR: Graph Transformer for Camera Re-localization

DOI：
10.1007/978-3-031-20080-9_14
发表时间：
2022
期刊：
影响因子：
0
作者：
Xinyi Li;Haibin Ling
通讯作者：
Xinyi Li;Haibin Ling

Transparent Object Tracking with Enhanced Fusion Module

DOI：
10.1109/iros55552.2023.10341597
发表时间：
2023-09
期刊：
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
影响因子：
0
作者：
Kalyan Garigapati;Erik Blasch;Jie Wei;Haibin Ling
通讯作者：
Kalyan Garigapati;Erik Blasch;Jie Wei;Haibin Ling

TracKlinic: Diagnosis of Challenge Factors in Visual Tracking