Recently, the Internet of Things (IoT) devices have been widely used in all sectors of society, especially in the field of wireless multimedia sensor networks (WMSNs). However, massive IoT video data requires efficient back-end processing to detect and track prominent objects for performing interconnection feedback. Correlation filter (CF)-based trackers present favorable properties for our back-end processing needs, such as fast computation speed, robustness to photometric and geometric variations. In this paper, we propose a novel background suppressed correlation filter (BSCF)-based target tracking method for wireless multimedia sensor networks, this method can incorporate all global background patches to enhance the tracking performance. Firstly, we reformulate the original ridge regression objective by introducing a convolution suppression term so that all real background patches will limit the generation of the filter through the circular shift operator and cropping operator. We then provide the closed form solutions of BSCF for multi-channel features via the alternating direction method of multipliers (ADMM). Further, we suggest a decomposition strategy to apply the background suppression framework as a general module to other CF-based trackers. The extensive experiments demonstrate that the proposed method performs favorably on multiple datasets against recent state-of-the-art methods. In particular, our BSCF ranks the first place on OTB-2013 with an AUC of 0.688 and an average precision of 0.904, exceeding the advanced CF tracker STRCF.

近年来，物联网（IoT）设备已广泛应用于社会的各个领域，尤其是在无线多媒体传感器网络（WMSN）领域。但是，大量的物联网视频数据需要高效的后端处理，以检测和跟踪突出的对象以执行互连反馈。基于相关滤波器（CF）的跟踪器为我们的后端处理需求提供了有利的属性，例如快速的计算速度，对光度和几何变化的鲁棒性。在本文中，我们提出了一种基于背景抑制相关滤波器（BSCF）的无线多媒体传感器网络目标跟踪方法，该方法可以结合所有全局背景补丁来提高跟踪性能。首先，我们通过引入卷积抑制项来重新构造原始的岭回归目标，以便所有实际背景色块都将通过循环移位算子和裁剪算子来限制滤波器的生成。然后，我们通过乘数的交替方向方法（ADMM）为多通道特征提供BSCF的闭式解决方案。此外，我们建议采用分解策略，将背景抑制框架作为通用模块应用于其他基于CF的跟踪器。广泛的实验表明，相对于最新的方法，该方法在多个数据集上的表现均令人满意。特别是，我们的BSCF在OTB-2013上排名第一，其AUC为0.688，平均精度为0.904，超过了先进的CF跟踪器STRCF。