Visual Tracking Using an Insect Vision Embedded Particle Filter
Visual Tracking Using an Insect Vision Embedded Particle Filter
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DOI：
10.1155/2015/573131
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发表时间：
2015-04
影响因子：
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通讯作者：
Wei Guo;Qingjie Zhao;Dongbing Gu
中科院分区：
文献类型：
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作者：
Wei Guo;Qingjie Zhao;Dongbing Gu
基于粒子滤波（PF）的目标跟踪算法受到了众多学者的关注。PF的核心是通过状态转移模型预测目标的可能位置。一种常用的方法是在平滑运动假设下诉诸先验运动线索，当目标以相对稳定的速度移动时，该方法表现良好。然而，如果目标经历突然运动，则可能失败。为了解决这个问题，受昆虫视觉的启发，我们提出了一个简单而有效的基于PF的视觉跟踪框架。利用昆虫视觉的神经元计算模型，我们以一种新的方式估计目标的运动，从而使用更准确的过渡模式来细化传播粒子的位置状态。此外，我们设计了一个新的样本优化框架，局部和全局搜索策略联合使用。此外，我们提出了一种新的方法来监测长时间的严重闭塞，我们可以恢复的目标。在公开的基准视频序列上的实验表明，该跟踪算法在具有挑战性的场景中，尤其是在跟踪突然运动或快速运动的目标时，性能优于现有的方法。
Particle filtering (PF) based object tracking algorithms have drawn great attention from lots of scholars. The core of PF is to predict the possible location of the target via the state transition model. One commonly adopted approach is resorting to prior motion cues under the smooth motion assumption, which performs well when the target moves with a relatively stable velocity. However, it would possibly fail if the target is undergoing abrupt motion. To address this problem, inspired by insect vision, we propose a simple yet effective visual tracking framework based on PF. Utilizing the neuronal computational model of the insect vision, we estimate the motion of the target in a novel way so as to refine the position state of propagated particles using more accurate transition mode. Furthermore, we design a novel sample optimization framework where local and global search strategies are jointly used. In addition, we propose a new method to monitor long duration severe occlusion and we could recover the target. Experiments on publicly available benchmark video sequences demonstrate that the proposed tracking algorithm outperforms the state-of-the art methods in challenging scenarios, especially for tracking target which is undergoing abrupt motion or fast movement.