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Tracking Algorithm of Multiple Pedestrians Based on Particle Filters in Video Sequences

机译：基于视频序列中粒子滤波器的多行人跟踪算法

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摘要

Pedestrian tracking is a critical problem in the field of computer vision. Particle filters have been proven to be very useful in pedestrian tracking for nonlinear and non-Gaussian estimation problems. However, pedestrian tracking in complex environment is still facing many problems due to changes of pedestrian postures and scale, moving background, mutual occlusion, and presence of pedestrian. To surmount these difficulties, this paper presents tracking algorithm of multiple pedestrians based on particle filters in video sequences. The algorithm acquires confidence value of the object and the background through extracting a priori knowledge thus to achieve multipedestrian detection; it adopts color and texture features into particle filter to get better observation results and then automatically adjusts weight value of each feature according to current tracking environment. During the process of tracking, the algorithm processes severe occlusion condition to prevent drift and loss phenomena caused by object occlusion and associates detection results with particle state to propose discriminated method for object disappearance and emergence thus to achieve robust tracking of multiple pedestrians. Experimental verification and analysis in video sequences demonstrate that proposed algorithm improves the tracking performance and has better tracking results.
机译：行人跟踪是计算机视野领域的关键问题。已经证明颗粒过滤器对于非线性和非高斯估计问题的行人跟踪非常有用。然而，由于行人姿势和规模，移动背景，相互闭塞和行人的存在，复杂环境中的行人跟踪仍然面临着许多问题。为了超越这些困难，本文介绍了基于视频序列粒子滤波器的多行人跟踪算法。算法通过提取先验知识来获取对象的置信度值和背景以实现多人检测;它采用颜色和纹理特征到粒子过滤器中以获得更好的观察结果，然后根据当前跟踪环境自动调整每个功能的权重值。在跟踪过程中，该算法处理严重的闭塞条件，以防止物体遮挡引起的漂移和损失现象，并将检测结果与颗粒状态联系起来，提出对象消失和出现的判断方法，从而实现了多个行人的鲁棒跟踪。视频序列中的实验验证和分析表明，所提出的算法提高了跟踪性能并具有更好的跟踪结果。

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