Zhang Jianming, Liu Yang, Liu Hehua, Wang Jin
Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Sensors (Basel). 2021 Feb 5;21(4):1129. doi: 10.3390/s21041129.
Visual object tracking is a significant technology for camera-based sensor networks applications. Multilayer convolutional features comprehensively used in correlation filter (CF)-based tracking algorithms have achieved excellent performance. However, there are tracking failures in some challenging situations because ordinary features are not able to well represent the object appearance variations and the correlation filters are updated irrationally. In this paper, we propose a local-global multiple correlation filters (LGCF) tracking algorithm for edge computing systems capturing moving targets, such as vehicles and pedestrians. First, we construct a global correlation filter model with deep convolutional features, and choose horizontal or vertical division according to the aspect ratio to build two local filters with hand-crafted features. Then, we propose a local-global collaborative strategy to exchange information between local and global correlation filters. This strategy can avoid the wrong learning of the object appearance model. Finally, we propose a time-space peak to sidelobe ratio (TSPSR) to evaluate the stability of the current CF. When the estimated results of the current CF are not reliable, the Kalman filter redetection (KFR) model would be enabled to recapture the object. The experimental results show that our presented algorithm achieves better performances on OTB-2013 and OTB-2015 compared with the other latest 12 tracking algorithms. Moreover, our algorithm handles various challenges in object tracking well.
视觉目标跟踪是基于摄像头的传感器网络应用中的一项重要技术。在基于相关滤波器（CF）的跟踪算法中全面使用的多层卷积特征取得了优异的性能。然而，在一些具有挑战性的情况下会出现跟踪失败的情况，因为普通特征无法很好地表示目标外观变化，并且相关滤波器更新不合理。在本文中，我们针对边缘计算系统捕获移动目标（如车辆和行人）提出了一种局部 - 全局多重相关滤波器（LGCF）跟踪算法。首先，我们构建一个具有深度卷积特征的全局相关滤波器模型，并根据宽高比选择水平或垂直划分来构建两个具有手工特征的局部滤波器。然后，我们提出一种局部 - 全局协作策略，在局部和全局相关滤波器之间交换信息。该策略可以避免目标外观模型的错误学习。最后，我们提出一种时空峰值旁瓣比（TSPSR）来评估当前CF的稳定性。当当前CF的估计结果不可靠时，将启用卡尔曼滤波器重新检测（KFR）模型来重新捕获目标。实验结果表明，与其他最新的12种跟踪算法相比，我们提出的算法在OTB - 2013和OTB - 2015上取得了更好的性能。此外，我们的算法能够很好地应对目标跟踪中的各种挑战。
