Automatic 3D tracking system for large swarm of moving objects
Automatic 3D tracking system for large swarm of moving objects
复制标题
针对大量移动物体的自动 3D 跟踪系统
DOI：
10.1016/j.patcog.2015.11.014
复制
发表时间：
2016-04
影响因子：
8
通讯作者：
Yan Qiu Chen
中科院分区：
文献类型：
--
作者：
Ye Liu;Shuohong Wang;Yan Qiu Chen
自然系统，如鸟群、鱼群和昆虫群，是由一大群移动的个体组成的。多年来，科学家们一直对它们表现出的复杂3D运动模式和动力学感兴趣，试图发现它们背后的启发性规则和原因。然而，由于缺乏有效的技术来精确测量个体的真实的三维运动轨迹，限制了对这些系统的定量研究。我们提出了一种自动跟踪系统，它能够跟踪大量的微小动物在一个3D体积与多个摄像头。由于有限的图像分辨率，这些目标的大部分视觉细节在捕获的图像中丢失，并且由于频繁的遮挡或运动模糊，其余部分很容易被破坏，这使得难以建立跨视图和跨帧对应关系。我们将问题表述为假设生成和验证的重复过程。假设产生时，跨视图匹配的模糊性发生，并验证在一个有效的3D跟踪阶段，目标是在3D空间建模和弱，但现有的视觉信息从多视图视频流被最大限度地收集。整个系统在处理可变数量的目标时是完全自动的，并且对检测和匹配误差具有鲁棒性。
Natural systems such as bird flocks, fish schools and insect swarms consist of a large group of moving individuals. For many years, scientists have been interested in the complex 3D motion patterns and dynamics they exhibit, trying to discover enlightening rules and causes behind them. Unfortunately, the lack of effective techniques to accurately measure the real 3D trajectories of the individuals had limited the quantitative study on these systems. We propose in this paper an automatic tracking system which is able to track a large number of tiny animals in a 3D volume with multiple cameras. Most visual details of such targets are lost in the captured images because of limited image resolution, and the remainder can be easily corrupted due to frequent occlusion or motion blur, which makes it difficult to establish cross-view and cross-frame correspondences. We formulate the problem as a repeated process of hypothesis generation and verification. Hypotheses are generated when cross-view matching ambiguities occur and are verified at an efficient 3D tracking stage where targets are modeled in 3D space and weak yet existing visual information from multi-view video streams are furthest collected. The whole system is fully automatic in dealing with variable number of targets and robust against detection and matching errors.
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DOI：
10.1109/iccv.2007.4409001
发表时间：
2007-12
期刊：
2007 IEEE 11th International Conference on Computer Vision
影响因子：
--
作者：
Hao Du;Danping Zou;Y. Chen
通讯作者：
Hao Du;Danping Zou;Y. Chen
DOI：
10.1109/cdc.1978.268125
发表时间：
1978
期刊：
1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes
影响因子：
--
作者：
D. Reid
通讯作者：
D. Reid
DOI：
10.1007/978-3-540-24670-1_3
发表时间：
2004-01-01
期刊：
COMPUTER VISION - ECCV 2004, PT 1
影响因子：
--
作者：
Okuma, K;Taleghani, A;Lowe, DG
通讯作者：
Lowe, DG
DOI：
10.1109/34.899946
发表时间：
2001
期刊：
IEEE Trans. Pattern Anal. Mach. Intell.
影响因子：
--
作者：
C. Veenman;M. Reinders;E. Backer
通讯作者：
C. Veenman;M. Reinders;E. Backer
DOI：
10.1109/iccv.2009.5459358
发表时间：
2009-09
期刊：
2009 IEEE 12th International Conference on Computer Vision
影响因子：
--
作者：
Danping Zou;Qi Zhao;H. Wu;Y. Chen
通讯作者：
Danping Zou;Qi Zhao;H. Wu;Y. Chen