基于多尺度混合模型多特征融合的单目标跟踪

Single Target Tracking with Multi-feature Fusion in Multi-scale Models

为实现高动态环境中的目标跟踪,本文提出了一种基于多尺度混合模型多特征融合的单目标跟踪算法。该算法自适应提取并融合多种图像特征从而实现复杂环境中的目标实时跟踪。针对图像目标的高动态特性及环境遮挡等问题,算法通过计算当前观测样本的置信度完成模板的自适应更新。利用国际计算机视觉学会目标跟踪数据库中具有典型特征的十个标准视频对跟踪算法进行测试。测试结果表明,在高动态环境及目标存在大变形情况下,本文提出的跟踪算法比同类算法的跟踪精度有显著提高。

To achieve robust target tracking in a highly dynamic scene, a single target tracking algorithm with multi-feature fusion in multi-scale models is proposed. The proposed models can adaptively fuse multiple features to achieve real time tracking in complex scenes. To tackle the problems of target significant deformation and occlusion, the proposed algorithm computes the confidence of the observation and uses it to update the reference models adaptively. The tracker is tested on ten representative sequences in a standard tracking benchmark. Compared with some other state-of-the-art algorithms, the results demonstrate that the tracking precision has been improved in the highly dynamic scenes with target significant deformation.