Du Chenjie, Lan Mengyang, Gao Mingyu, Dong Zhekang, Yu Haibin, He Zhiwei
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China.
Zhejiang Provincial Key Lab of Equipment Electronics, Hangzhou 310018, China.
Sensors (Basel). 2020 Jul 24;20(15):4124. doi: 10.3390/s20154124.
Although correlation filter-based trackers (CFTs) have made great achievements on both robustness and accuracy, the performance of trackers can still be improved, because most of the existing trackers use either a sole filter template or fixed features fusion weight to represent a target. Herein, a real-time dual-template CFT for various challenge scenarios is proposed in this work. First, the color histograms, histogram of oriented gradient (HOG), and color naming (CN) features are extracted from the target image patch. Then, the dual-template is utilized based on the target response confidence. Meanwhile, in order to solve the various appearance variations in complicated challenge scenarios, the schemes of discriminative appearance model, multi-peaks target re-detection, and scale adaptive are integrated into the proposed tracker. Furthermore, the problem that the filter model may drift or even corrupt is solved by using high confidence template updating technique. In the experiment, 27 existing competitors, including 16 handcrafted features-based trackers (HFTs) and 11 deep features-based trackers (DFTs), are introduced for the comprehensive contrastive analysis on four benchmark databases. The experimental results demonstrate that the proposed tracker performs favorably against state-of-the-art HFTs and is comparable with the DFTs.
尽管基于相关滤波器的跟踪器（CFT）在鲁棒性和准确性方面都取得了很大成就，但跟踪器的性能仍有提升空间，因为现有的大多数跟踪器要么使用单一的滤波器模板，要么使用固定的特征融合权重来表示目标。在此，本文提出了一种适用于各种挑战场景的实时双模板CFT。首先，从目标图像块中提取颜色直方图、方向梯度直方图（HOG）和颜色命名（CN）特征。然后，基于目标响应置信度利用双模板。同时，为了解决复杂挑战场景中的各种外观变化问题，将判别外观模型、多峰目标重新检测和尺度自适应方案集成到所提出的跟踪器中。此外，通过使用高置信度模板更新技术解决了滤波器模型可能漂移甚至损坏的问题。在实验中，引入了27个现有的竞争对手，包括16个基于手工特征的跟踪器（HFT）和11个基于深度特征的跟踪器（DFT），用于在四个基准数据库上进行综合对比分析。实验结果表明，所提出的跟踪器在性能上优于现有最先进的HFT，并且与DFT相当。
