Visual-attention gabor filter based online multi-armored target tracking

• Published in: 防务技术 2021, Vol.17 (4), p.1249-1261
• Main Authors: Fan-jie Meng, Xin-qing Wang, Fa-ming Shao, Dong Wang, Yao-wei Yu, Yi Xiao
• Format: Article
• Language: English
• ISSN: 2214-9147; 2214-9147
• DOI: 10.3969/j.issn.2214-9147.2021.04.013

The multi-armored target tracking (MATT) plays a crucial role in coordinated tracking and strike. The occlusion and insertion among targets and target scale variation is the key problems in MATT. Most state-of-the-art multi-object tracking (MOT) works adopt the tracking-by-detection strategy, which rely on compute-intensive sliding window or anchoring scheme in detection module and neglect the target scale variation in tracking module. In this work, we proposed a more efficient and effective spatial-temporal attention scheme to track multi-armored target in the ground battlefield. By simulating the structure of the retina, a novel visual-attention Gabor filter branch is proposed to enhance detection. By introducing temporal information, some online learned target-specific Convolutional Neural Networks (CNNs) are adopted to address occlusion. More importantly, we built a MOT dataset for armored targets, called Armored Target Tracking dataset (ATTD), based on which several comparable experiments with state-of-the-art methods are conducted. Experimental results show that the proposed method achieves outstanding tracking performance and meets the actual application requirements.