基于可靠特征点分配算法的鲁棒性跟踪框架（英文）

发布时间：2018-05-09 03:16

本文选题：局部最大小波系数 + 可靠特征点分配　；参考：《Frontiers of Information Technology & Electronic Engineering》2017年04期

【摘要】：视觉跟踪是近年来计算视觉最活跃的研究课题之一,已被广泛应用于许多视觉领域。然而,视觉跟踪技术仍然存在挑战,如目标发生光照变化、遮挡、外观形变等。为克服这些技术困难,本文提出基于小波变换的可靠特征点分配(Reliable point assignment,RPA)算法。通过搜索局部最大小波系数(Local maximal wavelet coefficients,LMWC)的位置,获得可靠特征点。在图像中,具有局部最大小波系数之处,表明该处图像信号发生了较大变化,因此,可靠特征点对图像噪声、光照变化和外观形变等情况都具有鲁棒性。此外,在检测中应用卡尔曼滤波器,以提高处理速度并减少误检率。最后,将所提出的RPA与卡尔曼滤波器集成到跟踪学习检测(Tracking learning detection,TLD)算法框架中,提高了跟踪精度,且降低了误检率。实验结果表明,新框架在精度、f值(f-measure)和平均重叠率(%)等方面均优于TLD和核化相关滤波器(KCF)这两个跟踪算法。
[Abstract]:Visual tracking is one of the most active research topics in computational vision in recent years and has been widely used in many fields of vision. However, there are still challenges in visual tracking, such as illumination change, occlusion, appearance deformation and so on. In order to overcome these technical difficulties, a reliable point assignment algorithm based on wavelet transform is proposed. The reliable feature points are obtained by searching the location of Local maximal wavelet coefficients (LMWCs). In the image, where the local maximum wavelet coefficients exist, it is shown that the image signal has changed greatly, so the reliable feature points are robust to the image noise, illumination change and appearance deformation. In addition, Kalman filter is used in detection to improve processing speed and reduce false detection rate. Finally, the proposed RPA and Kalman filter are integrated into the tracking tracking learning detection algorithm framework, which improves the tracking accuracy and reduces the false detection rate. The experimental results show that the new framework is superior to the two tracking algorithms, TLD and Kernel correlation filter, in terms of accuracy and average overlap ratio.
【作者单位】： School
【基金】：Project supported by the National Natural Science Foundation of China(Nos.61671213 and 61302058) the Guangzhou Key Lab of Body Data Science(No.201605030011)
【分类号】：TP391.41

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