Visual-attention gabor filter based online multi-armored target tracking |
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Authors: | Fan-jie Meng Xin-qing Wang Fa-ming Shao Dong Wang Yao-wei Yu Yi Xiao |
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Affiliation: | Department of Mechanical Engineering, College of Field Engineering, Army Engineering University of PLA, Nanjing 210007, China;College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210007, China |
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Abstract: | 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. |
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Keywords: | Multi-object tracking Deep learning Gabor filter Biological vision Military Application Video processing |
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