融合L2范数最小化和压缩Haar-like特征匹配的快速目标跟踪

吴正平; 杨杰; 崔晓梦; 张庆年

doi:10.11999/JEIT160122

融合L2范数最小化和压缩Haar-like特征匹配的快速目标跟踪

doi: 10.11999/JEIT160122

2.
(武汉理工大学光纤传感技术国家工程实验室武汉 430070) ②(三峡大学计算机与信息工程学院宜昌 443002)

基金项目:

国家自然科学基金(51479159)

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- 被引次数: 0
出版历程
- 收稿日期: 2016-01-26
- 修回日期: 2016-06-08
- 刊出日期: 2016-11-19

Fast Object Tracking Based on L2-norm Minimization andCompressed Haar-like Features Matching

2.
(The Key Laboratory of Fiber Optic Sensing Technology and Information Processing, Ministry of Education, Wuhan University of Technology, Wuhan 430070, China)

Funds:

The National Natural Science Foundation of China (51479159)

摘要

摘要: 在贝叶斯推理框架下，基于PCA子空间和L2范数最小化的目标跟踪算法能较好地处理视频场景中多种复杂的外观变化，但在目标出现旋转或姿态变化时易发生跟踪漂移现象。针对这一问题，该文提出一种融合L2范数最小化和压缩Haar-like特征匹配的快速视觉跟踪方法。该方法通过去除规模庞大的方块模板集和简化观测似然度函数降低计算的复杂度；而压缩Haar-like特征匹配技术则增强了算法对目标姿态变化及旋转的鲁棒性。实验结果表明：与目前流行的跟踪方法相比，该方法对严重遮挡、光照突变、快速运动、姿态变化和旋转等干扰均具有较强的鲁棒性，且在多个测试视频上可以达到29帧/s的速度，能满足快速视频跟踪要求。
- 目标跟踪 /
- PCA子空间 /
- L2范数最小化 /
- 压缩Haar-like特征 /
- 观测似然度
Abstract: Under the framework of the Bayesian inference, tracking methods based on PCA subspace and L2-norm minimization can deal with some complex appearance changes in the video scene successfully. However, they are prone to drifting or failure when the target object undergoes pose variation or rotation. To deal with this problem, a fast visual tracking method is proposed based on L2-norm minimization and compressed Haar-like features matching. The proposed method not only removes square templates, but also presents a simple but effective observation likelihood, and its robustness to pose variation and rotation is strengthened by Haar-like features matching. Compared with other popular method, the proposed method has stronger robustness to abnormal changes (e.g. heavy occlusion, drastic illumination change, abrupt motion, pose variation and rotation, etc). Furthermore, it runs fast with a speed of about 29 frames/s.
- Object tracking /
- PCA subspace /
- L2-norm minimization /
- Compressed Haar-like feature /
- Observation likelihood

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参考文献(19)

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