Acquiring 3D motion trajectories of large numbers of swarming animals
Acquiring 3D motion trajectories of large numbers of swarming animals
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DOI：
10.1109/iccvw.2009.5457649
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发表时间：
2009-09
期刊：
影响因子：
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通讯作者：
H. Wu;Qi Zhao;Danping Zou;Y. Chen
中科院分区：
文献类型：
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作者：
H. Wu;Qi Zhao;Danping Zou;Y. Chen
虫群、鸟群、鱼群等动物群体的社会行为多年来引起了许多领域科学家的浓厚兴趣。获取群体中每个个体的 3D 运动轨迹对于定量研究此类行为至关重要，但由于个体数量众多、视觉特征相似和频繁的遮挡，这项任务具有挑战性。在本文中，我们提出了一种新颖的方法，通过将其表述为三个线性分配问题（LAP），为该任务提供全局最优结果。第一个LAP通过空间全局分配获得视频序列中粒子的2D轨迹；第二个利用最大对极联动长度（MECL）来有效消除匹配歧义；最后一个通过时空全局分配将轨道段链接成完整的 3D 轨迹。所提出的匹配成本 MECL 对整个轨迹期间的全局运动信息进行编码，并且能够处理由第一个 LAP 产生的关联错误。我们的方法计算效率高，并且在 PC 上几乎实时运行。模拟粒子群的实验结果验证了该方法的准确性和效率。作为现实世界的案例，我们成功获得了由数百只个体组成的果蝇群的 3D 轨迹，据我们所知，这是第一个此类成就。
Social behavior of animal group, such as insect swarm, bird flock, fish school, has captivated strong interest of scientists in many fields for years. Acquiring 3D motion trajectory of each individual in a swarm is vital for quantitative study of such behavior, yet this task is challenging due to large numbers of individuals, similar visual feature and frequent occlusions. In this paper, we present a novel approach which provides global optimal results for this task by formulating it as three linear assignment problems (LAP). The first LAP obtains the 2D tracks of particles in video sequences via spatially global assignment; the second one utilizes maximum epipolar co-motion length (MECL) to effectively eliminate matching ambiguities; the last one links the track segments into complete 3D trajectories via spatial-temporal global assignment. The proposed matching cost MECL encodes the global motion information during the whole track and is able to handle the association errors resulting from the first LAP. Our method is computationally efficient and works in near real time on a PC. Experiment results on simulated particle swarms validated the accuracy and efficiency of the proposed method. As real-world case, we successfully acquired 3D trajectories of Drosophila melanogaster (fruit fly) swarm comprising hundreds of individuals, which to our best knowledge is the first such achievement.