文章基本信息

标题：Visual-attention gabor filter based online multi-armored target tracking
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作者：Fan-jie Meng ; Xin-qing Wang ; Fa-ming Shao 等
期刊名称：Defence Technology
印刷版ISSN：2214-9147
出版年度：2021
卷号：17
期号：4
页码：1249-1261
DOI：10.1016/j.dt.2020.06.013
出版社：Elsevier B.V.
摘要：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.
关键词：Multi-object tracking ; Deep learning ; Gabor filter ; Biological vision ; Military ; Application ; Video processing