Model Update Particle Filter for Multiple Objects Detection and Tracking
Model Update Particle Filter for Multiple Objects Detection and Tracking
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DOI：
10.1109/iptc.2011.10
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Yunji Zhao;Hailong Pei
中科院分区：
文献类型：
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作者：
Yunji Zhao;Hailong Pei
多目标跟踪是一项具有挑战性的任务。本文提出了一种多目标检测与跟踪算法，并自动更新目标模型。本文的主要贡献如下：首先，采用颜色直方图（HC）和方向梯度直方图（HOG）表示目标，在卡尔曼滤波和高斯模型的框架下实现模型更新;其次，采用高斯混合模型（GMM）和Bhattacharyya距离检测目标外观。结合特征和模型更新机制的粒子滤波器可以提高跟踪效果。对视频序列的实验表明，基于改进算法的多目标跟踪具有良好的性能。
Multiple objects tracking is a challenging task. This article presents an algorithm which can detect and track multiple objects, and update target model automatically. The contributions of this paper as follow: Firstly, we use color histogram(HC) and histogram of orientated gradients(HOG) to represent the objects, model update is realized under the frame of kalman filter and gaussian model, secondly we use Gaussian Mixture Model(GMM) and Bhattacharyya distance to detect object appearance. Particle filter with combined features and model update mechanism can improve tracking effects. Experiments on video sequences demonstrate that multiple objects tracking based on improved algorithm have good performance.