| 刘秀平,袁 皓,李梦璐,王圣鹏,徐 健,张立昌,闫焕营.ECO 多特征融合目标工件跟踪方法研究[J].电子测量与仪器学报,2021,35(10):161-167 |
| ECO 多特征融合目标工件跟踪方法研究 |
| Research on target work-piece tracking method based onECO multi-feature fusion |
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| DOI： |
| 中文关键词: ECO 工件跟踪 特征融合 相关滤波 |
| 英文关键词:ECO work-piece tracking feature fusion correlation filtering |
| 基金项目:陕西省科技厅项目(2018GY 173)、西安市科技局项目(GXYD75)资助 |
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| 中文摘要: |
| 针对复杂环境中目标工件跟踪精度不高的问题,提出了一种基于 ECO 改进的目标工件跟踪方法。 首先基于 ECO 相关
滤波器框架,采用 VGG 特征与传统手工特征加权融合的方法,有效提高目标工件跟踪精度;然后,利用快速判别尺度空间跟踪
器实现对目标工件的尺度自适应跟踪;最后,引入一种高置信度更新指标确定跟踪模型的稀疏更新策略,提高算法鲁棒性。 在
OTB-2015 标准数据集上进行测试,并与其他主流跟踪算法进行对比,实验结果表明,该算法的平均跟踪精度和平均重叠精度均
为最优,分别达到 89. 2%和 68. 6%;对于使用 CCD 工业相机拍摄的目标工件数据集,同样具备良好的跟踪性能,进一步验证了
算法有效性。 |
| 英文摘要: |
| Aiming at the low tracking accuracy of target work-piece in complex environment, an improved tracking method for target workpiece based on ECO is presented. Firstly, based on the framework of eco correlation filter, VGG features and traditional manual features
are weighted and fused to improve the tracking accuracy. Then, the fast discriminant scale space tracker is used to track the target workpiece adaptively. Finally, a high confidence update index is introduced to determine the sparse update strategy of the tracking model to
improve the robustness of the algorithm. Tested on the OTB - 2015 standard dataset and compared with other mainstream tracking
algorithms, the experimental results show that the average tracking accuracy and the average overlap accuracy of the algorithm are both
optimal, reaching 89. 2% and 68. 6%, This algorithm also has good tracking performance for target work-piece dataset taken with CCD
industrial camera, which further verifies the validity of the algorithm. |
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