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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >A Bayesian plan-view map based approach for multiple-person detection and tracking
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A Bayesian plan-view map based approach for multiple-person detection and tracking

机译：基于贝叶斯平面视图地图的多人检测和跟踪方法

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This work proposes a novel approach for people detection and tracking in colour-with-depth sequences using a particle filtering approach. Detection and tracking are performed in plan-view maps integrating occupancy and height information with a novel plan-view map representation for colour information. Using the three maps, we propose a Multiple particle filtering algorithm for people detection and tracking. The observation model proposed integrates information from the three maps so that people with different coloured clothes are not confused even when they interact at close distances. To avoid the coalescence problem when people with similar coloured clothes approach each other, the weight of particles is modified by an interaction factor that combines colour and position information. The algorithm also avoids the coalescence problem in case of total occlusion by means of an occlusion detection and recovering mechanism. Finally, a solution is proposed to improve the exponential complexity of multiple particle filters so that the algorithm proposed has linear complexity. The approach proposed has been tested in several colour-with-depth sequences where people move and interact freely in the environment. In the sequences, people walk at different distances, cross their paths causing frequent occlusions, jump, run and have close interactions such as shaking hands or embracing each other. The experimental results show that our proposal is able to detect and keep track of every person with a low error and deals with partial and total occlusions. Besides, the detection and tracking techniques presented are appropriate for large tracking problems in real-time applications since their complexity is linear, are suitable for parallel processing and allow the integration of information provided by multiple stereo vision sensors. (C) 2008 Elsevier Ltd. All rights reserved.
机译：这项工作提出了一种新颖的方法，可以使用粒子过滤方法来检测和跟踪颜色随深度变化的人群。在将占用和高度信息与用于颜色信息的新颖的平面图表示形式相结合的平面图图中执行检测和跟踪。使用这三个地图，我们提出了一种用于人员检测和跟踪的多重粒子滤波算法。提出的观察模型整合了来自三个地图的信息，因此，即使穿着不同颜色的衣服的人近距离互动，也不会感到困惑。为了避免穿着相似颜色衣服的人接近时的合并问题，可以通过将颜色和位置信息结合在一起的交互因子来修改粒子的重量。该算法还通过遮挡检测和恢复机制避免了在完全遮挡的情况下的合并问题。最后，提出一种解决方案，以提高多个粒子滤波器的指数复杂度，使所提出的算法具有线性复杂度。所提议的方法已在几种颜色随深度变化的序列中进行了测试，其中人们可以在环境中自由移动和交互。在这些序列中，人们以不同的距离行走，穿过他们的路径导致频繁的闭塞，跳跃，奔跑，并有紧密的互动，例如握手或互相拥抱。实验结果表明，我们的建议能够检测并跟踪每个人的错误率较低，并且可以处理部分遮挡和全部遮挡。此外，提出的检测和跟踪技术适用于实时应用中的大型跟踪问题，因为它们的复杂性是线性的，适用于并行处理并允许集成多个立体视觉传感器提供的信息。 （C）2008 Elsevier Ltd.保留所有权利。

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