光电工程, 2016, 43 (3): 58, 网络出版: 2016-09-12   

自适应多特征融合目标跟踪

Target Tracking Based on Adaptive Fusion of Multi-feature
作者单位
江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122
摘要
针对目标跟踪在复杂场景中鲁棒性较差以及有效性较低的问题,基于在线检测跟踪框架提出一种基于回归的自适应多特征融合目标跟踪算法。对密集采样得到的各子图像块提取出多种特征分别建立目标表观模型,通过正则化最小二乘分类器得到各模型的响应,利用加权和准则融合各响应,通过求解岭回归方程自适应地在线更新各响应权重以增强局部判别力,得到精确而稳定的检测分数值,从而进行有效鲁棒地跟踪。实验结果表明,该算法在大多数复杂场景下其跟踪精度和鲁棒性优于现有的目标跟踪算法。
Abstract
To solve the problem of poor robustness and low effectiveness of target tracking in complex scenes, a target tracking algorithm based on adaptive multi-feature fusion in tracking-by-detection framework is proposed. Features are extracted from the sub-images extracted by dense sampling, and the target appearance models are established respectively. The response of each model is obtained with regularized least squares classifier. The final response is achieved by weighted sums of the responses, in which the weights are updated by solving a regression equation. It helps to obtain accurate and stable detection scores by enhancing local discrimination. Experimental results show that the algorithm outperforms other state-of-the-art tracking algorithms in tracking accuracy and robustness in most complex scenes.

刘行, 陈莹. 自适应多特征融合目标跟踪[J]. 光电工程, 2016, 43(3): 58. LIU Xing, CHEN Ying. Target Tracking Based on Adaptive Fusion of Multi-feature[J]. Opto-Electronic Engineering, 2016, 43(3): 58.

本文已被 3 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!