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首页> 外文会议>International Conference on Computer Vision Theory and Applications >Particle Filter using Motion Direction for Vehicle Tracking
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Particle Filter using Motion Direction for Vehicle Tracking

机译：使用用于车辆跟踪的运动方向的粒子滤波器

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In this paper, a new approach for particle filter (PF) is presented. Color histogram information is used for defining the state model of target. Since we deal with vehicle tracking problem, the information of direction of the vehicle is important. This algorithm has several steps. First one is obtaining the motion direction of the target object. The second one is calculating the angle differences between the direction and each candidate sample. In the last step, the probability of each sample will be weighted according to its angular distance to the motion direction of the target. So, the samples moving in the similar direction with the target, will get larger weights, and increase the probabilities of estimating the state parameters of the target. By using this algorithm, PF became more stable and robust against noises and occlusions. We showed that the proposed PF increases the tracking performance and robustness against noises where as the computational burden is decreased.
机译：本文提出了一种粒子滤波器（PF）的新方法。颜色直方图信息用于定义目标的状态模型。由于我们处理车辆跟踪问题，因此车辆方向的信息很重要。该算法有几个步骤。首先是获得目标对象的运动方向。第二个是计算方向和每个候选样本之间的角度差异。在最后一步中，每个样品的概率将根据其与目标的运动方向的角度距离而加权。因此，在与目标相似的方向上移动的样本将获得更大的权重，并增加估计目标的状态参数的概率。通过使用该算法，PF变得更加稳定和抗噪声和闭塞。我们认为，所提出的PF增加了对计算负担降低的噪声的跟踪性能和鲁棒性。

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