纵横比自适应的时空正则化相关滤波算法

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中文摘要：在目标跟踪中,传统相关滤波算法无法感知运动目标尺度纵横比变化,且易受复杂环境影响导致跟踪失败.为此,提出了纵横比自适应的时空正则化相关滤波算法.首先,参考平均峰值相关能量(Average peak-to-correlation energy,APCE)和响应峰值对每个特征的响应图进行加权融合,以实现对目标的精确跟踪.其次,结合近正交性和空间正则化提出一种新的一维边界滤波器,通过定位目标包围框的四个边界位置实现对目标尺度和纵横比变化的自适应检测,有效抑制了边界效应带来的负面影响.最后,根据响应输出的峰值旁瓣比(Peak-to-sidelobe ratio,PSR)独立地调节各边界滤波器的学习率,防止模型退化.在OTB数据集上进行了测试,该算法表现出理想的跟踪效果,在各个挑战属性上相较于其他优秀算法均取得了更优结果.

外文标题：Spatial-temporal regularized correlation filtering algorithm with adaptive aspect ratio

外文摘要：In object tracking, the traditional correlation filtering algorithm is unable to perceive the change of scale aspect ratio for moving targets, and it is easily affected by a complex environment, resulting in tracking failure. Therefore, a spatial-temporal regularized correlation filtering algorithm with adaptive aspect ratio(AAR-SRCF) was proposed. Firstly, the average peak-to-correlation energy (APCE) and peak score were used as references to weigh and fuse each feature response map to achieve accurate results. Additionally, a set of novel one-dimensional boundary filters were presented, integrating near-orthogonality and spatial regularization. These filters can adaptively detect changes in the target scale and aspect ratio by precisely locating the boundaries of the target's bounding box. Moreover, spatial regularization effectively mitigated the negative impact of the boundary effect for boundary filters. Finally, the learning rate of each boundary filter was adjusted separately according to the peak-to-sidelobe ratio (PSR) to prevent the model from degradation. Through extensive experiments on OTB datasets, the proposed algorithm shows excellent tracking performance, achieving better results than other excellent algorithms in each challenge attribute.

外文关键词：

object trackingcorrelation filterspatial regularizationadaptive aspect ratiotemplate updateresponse fusion

作者：

许凯、李婷、葛洪伟

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作者单位：

江南大学人工智能与计算机学院,江苏无锡 214122

江苏省模式识别与计算智能工程实验室(江南大学),江苏无锡 214122

关键词：

目标跟踪相关滤波空间正则化自适应纵横比模板更新响应融合

基金：

National Natural Science Foundation of ChinaJiangsu University Superior Discipline Construction ProjectTalent Introduction Project

项目编号：

61806006B12018

出版年：

2024

DOI：

10.62756/jmsi.1674-8042.2024002

测试科学与仪器

中北大学

测试科学与仪器

影响因子：0.111

ISSN：1674-8042

年,卷(期)：2024.15(1)

参考文献量24