Knowledge Commons of Institute of Automation,CAS
Greedy Batch-Based Minimum-Cost Flows for Tracking Multiple Objects | |
Wang, Xinchao1; Fan, Bin2![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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2017-10-01 | |
卷号 | 26期号:10页码:4765-4776 |
文章类型 | Article |
摘要 | Minimum-cost flow algorithms have recently achieved state-of-the-art results in multi-object tracking. However, they rely on the whole image sequence as input. When deployed in real-time applications or in distributed settings, these algorithms first operate on short batches of frames and then stitch the results into full trajectories. This decoupled strategy is prone to errors because the batch-based tracking errors may propagate to the final trajectories and cannot be corrected by other batches. In this paper, we propose a greedy batch-based minimum-cost flow approach for tracking multiple objects. Unlike existing approaches that conduct batch-based tracking and stitching sequentially, we optimize consecutive batches jointly so that the tracking results on one batch may benefit the results on the other. Specifically, we apply a generalized minimum-cost flows (MCF) algorithm on each batch and generate a set of conflicting trajectories. These trajectories comprise the ones with high probabilities, but also those with low probabilities potentially missed by detectors and trackers. We then apply the generalized MCF again to obtain the optimal matching between trajectories from consecutive batches. Our proposed approach is simple, effective, and does not require training. We demonstrate the power of our approach on data sets of different scenarios. |
关键词 | Multi-object Tracking Minimum-cost Flows Batch Processing Graph Transformation |
WOS标题词 | Science & Technology ; Technology |
DOI | 10.1109/TIP.2017.2723239 |
关键词[WOS] | MULTITARGET TRACKING ; PROPAGATION ; MODELS |
收录类别 | SCI |
语种 | 英语 |
项目资助者 | Swiss National Science Foundation ; Natural Science Foundation of China(61573352 ; Australian Research Council(FT-130101457 ; 61403375 ; DP-140102164 ; 61472119) ; LP-150100671) |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000406329500014 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/19694 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Fan, Bin |
作者单位 | 1.Univ Illinois, Beckman Inst, Image Format & Proc Grp, Urbana, IL 61801 USA 2.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 3.IBM Thomas J Watson Res Ctr, Yorktown Hts, NY 10598 USA 4.Texas A&M Univ, Dept Comp Sci & Engn, College Stn, TX 77843 USA 5.Facebook Inc, San Francisco, CA 94025 USA 6.UBTech Sydney Artificial Intelligence Inst, Sydney, NSW 2008, Australia 7.Univ Sydney, Fac Engn & Informat Technol, Sch Informat Technol, Sydney, NSW 2008, Australia |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Wang, Xinchao,Fan, Bin,Chang, Shiyu,et al. Greedy Batch-Based Minimum-Cost Flows for Tracking Multiple Objects[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(10):4765-4776. |
APA | Wang, Xinchao.,Fan, Bin.,Chang, Shiyu.,Wang, Zhangyang.,Liu, Xianming.,...&Bin Fan.(2017).Greedy Batch-Based Minimum-Cost Flows for Tracking Multiple Objects.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(10),4765-4776. |
MLA | Wang, Xinchao,et al."Greedy Batch-Based Minimum-Cost Flows for Tracking Multiple Objects".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.10(2017):4765-4776. |
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Tracking-TIP.pdf(2637KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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