A robust object tracking framework based on a reliable point assignment algorithm

Visual tracking, which has been widely used in many vision fields, has been one of the most active research topics in computer vision in recent years. However, there are still challenges in visual tracking, such as illumination change, object occlu- sion, and appearance deformation. To overcome these difficulties, a reliable point assignment （RPA） algorithm based on wavelet transform is proposed. The reliable points are obtained by searching the location that holds local maximal wavelet coefficients. Since the local maximal wavelet coefficients indicate high variation in the image, the reliable points are robust against image noise, illumination change, and appearance deformation. Moreover, a Kalman filter is applied to the detection step to speed up the detection processing and reduce false detection. Finally, the proposed RPA is integrated into the tracking-learning-detection （TLD） framework with the Kalman filter, which not only improves the tracking precision, but also reduces the false detections. Experimental results showed that the new framework outperforms TLD and kernelized correlation filters with respect to precision, f-measure, and average overlap in percent.

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。