Visual object tracking has made significant contributions in many practical applications, but it remains a great challenge when the camera moves/shakes or the target is occluded. Various solutions leveraging deep-learning (DL) techniques have been introduced to address these challenging factors. However, these DL-based methods can hardly be implemented on an edge computing platform due to its limited computational resources. In this study, we propose a lightweight and robust cross-modal fusion RGB-T object tracker for edge computing platforms based on Jitter Factor and associated Kalman filter. In the proposed tracker, visible and infrared features of the target are extracted and fused using a cross-modal fusion strategy based on the modal reliability. Meanwhile, the newly proposed Jitter Factor, derived from image morphology, is used to judge the motion of camera. Once the camera motion is detected, target position would be corrected via global image registration and associated Kalman filter. Experimental results on RGBT234 and GTOT datasets indicates that the proposed lightweight tracking method outperforms other non-DL-based tracking methods. For the problem of camera motion, it exhibits a competitive performance among other DL-based trackers, but with faster speed (25 FPS using only a CPU).

中文翻译：

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基于抖动因子和相关卡尔曼滤波器的轻量级鲁棒 RGB-T 对象跟踪器


视觉对象跟踪在许多实际应用中做出了重大贡献，但当相机移动/抖动或目标被遮挡时，它仍然是一个巨大的挑战。已经引入了各种利用深度学习 （DL） 技术的解决方案来解决这些具有挑战性的因素。然而，由于计算资源有限，这些基于 DL 的方法几乎无法在边缘计算平台上实现。在本研究中，我们提出了一种基于抖动因子和相关卡尔曼滤波器的轻量级、鲁棒的边缘计算平台跨模态融合RGB-T目标跟踪器。在所提出的跟踪器中，使用基于模态可靠性的跨模态融合策略提取和融合目标的可见光和红外特征。同时，新提出的 Jitter Factor 源自图像形态学，用于判断相机的运动。一旦检测到相机运动，目标位置将通过全局图像配准和相关的卡尔曼滤波器进行校正。在 RGBT234 和 GTOT 数据集上的实验结果表明，所提出的轻量级跟踪方法优于其他非基于 DL 的跟踪方法。对于相机运动问题，它表现出与其他基于 DL 的跟踪器相比具有竞争力的性能，但速度更快（仅使用 CPU 即可达到 25 FPS）。