A boosted particle filter: Multitarget detection and tracking
A boosted particle filter: Multitarget detection and tracking
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DOI：
10.1007/978-3-540-24670-1_3
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发表时间：
2004-01-01
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影响因子：
--
通讯作者：
Lowe, DG
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文献类型：
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作者：
Okuma, K;Taleghani, A;Lowe, DG
跟踪不同数量的非刚性物体的问题有两个主要的困难。首先，观测模型和目标分布可以是高度非线性和非高斯的。第二，大量不同数量的对象的存在会产生复杂的相互作用，并产生重叠和模糊。为了克服这些困难，我们引入了一个视觉系统，能够学习，检测和跟踪感兴趣的对象。该系统被证明在使用视频序列跟踪曲棍球运动员的上下文中。我们的方法结合了两个成功的算法的优势：混合粒子滤波器和Adaboost。混合粒子滤波器[17]非常适合多目标跟踪，因为它为每个玩家分配一个混合分量。混合粒子滤波器的关键设计问题是建议分布的选择和对象离开和进入场景的处理。在这里，我们使用混合模型构建提案分布，该模型包含来自每个玩家的动态模型和Adaboost生成的检测假设的信息。学习的Adaboost建议分布使我们能够快速检测进入场景的玩家，而过滤过程使我们能够跟踪单个玩家。Adaboost与混合粒子滤波器的交错结果是一个简单，但功能强大的全自动多目标跟踪系统。
The problem of tracking a varying number of non-rigid objects has two major difficulties. First, the observation models and target distributions can be highly non-linear and non-Gaussian. Second, the presence of a large, varying number of objects creates complex interactions with overlap and ambiguities. To surmount these difficulties, we introduce a vision system that is capable of learning, detecting and tracking the objects of interest. The system is demonstrated in the context of tracking hockey players using video sequences. Our approach combines the strengths of two successful algorithms: mixture particle filters and Adaboost. The mixture particle filter [17] is ideally suited to multi-target tracking as it assigns a mixture component to each player. The crucial design issues in mixture particle filters are the choice of the proposal distribution and the treatment of objects leaving and entering the scene. Here, we construct the proposal distribution using a mixture model that incorporates information from the dynamic models of each player and the detection hypotheses generated by Adaboost. The learned Adaboost proposal distribution allows us to quickly detect players entering the scene, while the filtering process enables us to keep track of the individual players. The result of interleaving Adaboost with mixture particle filters is a simple, yet powerful and fully automatic multiple object tracking system.