Faster MDNet for Visual Object Tracking
Qianqian Yu, Keqi Fan, Yiyang Wang, Yuhui Zheng · Applied Sciences · 2022
With the rapid development of deep learning techniques, new breakthroughs have been made in deep learning-based object tracking methods. Although many approaches have achieved state-of-the-art results, existing methods still cannot fully satisfy practical needs. A robust tracker should perform well in three aspects: tracking accuracy, speed, and resource consumption. Considering this notion, we propose a novel model, Faster MDNet, to strike a better balance among these factors. To improve the tracking accuracy, a channel attention module is introduced to our method. We also design domain adaptation components to obtain more generic features. Simultaneously, we implement an adaptive, spatial pyramid pooling layer for reducing model complexity and accelerating the tracking speed. The experiments illustrate the promising performance of our tracker on OTB100, VOT2018, TrackingNet, UAV123, and NfS.