基于局部背景感知的目标跟踪

储珺; 杜立辉; 汪凌峰; 潘春洪

doi:10.3724/SP.J.1004.2012.01985

基于局部背景感知的目标跟踪

doi: 10.3724/SP.J.1004.2012.01985

1.
南昌航空大学软件学院南昌 330063;
2.
南昌航空大学信息工程学院南昌 330063;
3.
中国科学院自动化研究所模式识别国家重点实验室北京 100190

详细信息

通讯作者:
储珺

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- 被引次数: 0
出版历程
- 收稿日期: 2011-10-08
- 修回日期: 2012-03-26
- 刊出日期: 2012-12-20

Local Background-aware Target Tracking

1.
School of Software, Nanchang Hangkong University, Nanchang 330063;
2.
School of Information Engineering, Nanchang Hangkong University, Nanchang 330063;
3.
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190

摘要

摘要: 经典视觉跟踪方法通常仅以目标区域内信息作为目标描述. 实际中, 目标局部背景信息也影响着跟踪性能. 本文首先在目标描述中引入局部背景信息, 并将目标表示为一带权点集. 然后通过K近邻计算目标观测概率, 并联合目标先验信息得到搜索区域内各点后验概率值. 最后, 利用均值漂移(Mean shift)算法估计目标状态. 本文算法优点如下: 1) 目标描述中联合局部背景信息, 增强了目标模型. 因此, 跟踪过程中提高了目标与背景的区分能力, 并进一步使跟踪算法更加稳定, 跟踪结果更加精准. 2)目标初始化时, 利用Mean shift对目标进行一次重定位. 由此解决了不精确初始化时跟踪算法容易失效的问题. 在不同视频上进行了定性和定量的实验验证. 结果表明本文算法具有较高的跟踪稳定性和准确性, 尤其当目标初始化比较粗糙时.
- 目标跟踪 /
- 带权点集 /
- K近邻 /
- 均值漂移
Abstract: Classical visual tracking methods usually only adopt target information to describe the target. In practice, local background information around the target also influences the tracking process. In this paper, we first introduce the local background information into target description, and represent it as a set of weighted feature points. After that, the posterior probability of each point in the search-region is calculated by incorporating the target observation obtained by K nearest neighbor (KNN) algorithm and the Gaussian prior distribution of the target. Finally, the mean shift algorithm is used to estimate the target state. The proposed method has the following two advantages: 1) The local background information is integrated into the target description, which enhances the target model. Thereby, the discriminative ability is promoted in the tracking process, which further makes the tracker more robust and accurate. 2) In the initialization stage, the mean-shift is applied to relocating the target, which can solve the problem that the tracking algorithm is prone to failure in inexact initialization. Extensive experiments in different video sequences are conducted to evaluate our approach qualitatively and quantitatively. The results show that our method holds high tracking accuracy and stability, especially when the target is roughly initialized.
- Target tracking /
- weighted points set /
- K nearest neighbor (KNN) /
- mean shift

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参考文献(1)

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